A user-centered approach to develop a new type of event-based social network

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POLITECNICO DI MILANO

School of Industrial and Information Engineering Master of Science in

Management Engineering

A user-centered approach to develop a

new type of Event-Based Social Network

Advisor: Prof. Barbara Pernici

Thesis of: Matteo Milanese, mat. 878570 Paolo Pierri, mat. 881361

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Abstract

This thesis is a first step towards a big objective: the developing of a user-centered Event-Based Social Network (EBSN) leveraging on a recommen-dation system able to suggest people with whom attend public or private events. The main idea is to transform online traffic into offline relationships through a design-driven innovation into the EBSN field. Moreover, the pro-posed model automatically investigates new possibilities to help people find-ing higher satisfactory states, providfind-ing both the information about people around and events where it would be possible to meet them. A user-centered approach would disruptively change the digital interactions paradigm. This is possible not only because of the rise of new technologies, both software and hardware oriented, but also because people’s need of getting connected is not completely satisfied by actual social networks. Therefore, our pro-posal would allow people to build and join specific communities according to their particular interests and attitudes, through both individual actions and customized platform stimuli. Eventually, the aim of our digital platform is to enhance the quality of people’s life leveraging on the most important aspect for every human being: the relationships network.

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Introduction

“Every one tend to become similar to the average of the five people he spend more time with.”

J. Rohn, 2012

Social Networks (SNs) satisfy the most important needs of every human being living in a modern country. According to the theory of Maslow (Fig. 1.1), after the physiological and safety needs also named basic needs, every person tends to satisfy the psychological needs of belonging and self esteem. Social networks, generally speaking, address in its essence both the need of belonging and self esteem. This is the reason why their development has been so disruptive in the 21st century.

First generation of SNs can be defined as follow: they are system useful for the distribution of content among people who care about each other. Thanks to SNs and social media in general, everyone can connect with people from all over the world, for free. Moreover, social networks help to fortify and maintain your personal relationships, even with people who are far away from you and allow people to get connected in any place at any time. This boosts the sense of belonging due to the fact that getting connected is a lot easier.

On Facebook, the relationships are established and enhanced thanks to the ”posting” feature. Facebook, Instagram, Twitter, and LinkedIn are based on the sharing of contents by users and therefore are called Content-Based Social Network (CBSN).

”What do you think?”

This is what every one can read in the main feed of its own Facebook page. And why do people share information on SNs? Every answer to this question would not be complete because there are infinite different cases. Anyhow,

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Figure 1.1: Maslow pyramid

in our opinion, one of the most exhaustive answer is: vanity! People share online content that makes them look valuable. People want to communicate to others that they are well educate, have a good social position, partici-pated to cool parties, and dated attractive people. This is common and not difficult to understand; everyone needs to enhance its sense of esteem and wants to be accepted by the society (self esteem need). To engage people even more, Facebook introduced the feedback responses of ”like” and ”love”. Therefore, people are being judged for the numbers of positive feedback they receive. This abnormality has reached its peak with Instagram. Everyone tries to simulate who he is not. The worse thing is that, while on Facebook at least you have ”friends”, on Instagram you do not have friends anymore: you have ”followers” and ”followings”. It can seem not relevant, but since the spread and the usage of Instagram, this idea of ”follow” is drastically changing the social life of many young people. There is a ”psychological rush of first posting the photo and then receiving positive feedback ”. The conse-quences are already visible. There is a price young generations are paying for being connected so easily through digital media.

Briefly, other collateral effects related to the wrong usage of CBSNs are: the spread of fake profiles that boost the fear of unknown and negatively

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affect the chances of people to meet in real life situations. The ”hater” occurrence. The phenomenon of the tailored advertising thanks to which we have become products from a business model perspective. Finally, fake news spread on SNs are changing the quality of information for many people. Despite all this, are social networks like Facebook and Instagram satisfying the necessity of people of being connected? Or are they producing the op-posite effects? Which kind of benefits people have in their real life using these SNs? LinkedIn has a precise purpose, it surely adds value to the busi-ness/career sphere of life; but, Instagram? Do people have more significant relationships thanks to it?

Bauman would probably not be surprised, he who had coined ”The solitude of the global citizen”, to observe how loneliness and psychological distress are becoming critical factors for many people. We are continuously con-nected digitally with everyone, yet the web and social networks seem to increase the solitude of people where virtual relationships end up replacing real exchanges. On the web everything is easy, instant; you declare yourself as a friend and you ignore him a moment later. These are the ephemeral relationships that establish ”liquid society”, to quote Bauman again, where nothing is forever, everything is constantly changing, and what is no longer working today is thrown away to find something new according to a con-sumerist logic that seems to influence nowadays also human relationships. Recently, another social network’s category appeared in the industry. It comprehends dating applications such as Tinder, Meetic, Badoo, Lovoo, and Happn. The characteristic of these applications is that the purpose of users is not limited to digital connections enhanced by the distribution of content, but it is real life oriented. The first definition proposed about SNs (systems for distributing content among people who care about each other) has to change considering the innovation provided by dating applications. Therefore, SNs can be generally defined as ”systems that allow people to connect each other”.

Dating application users want to know other people with whom to share real life activities such as special dinners, parties, pic-nics, etc. The driving need is always the same: stay connected. But, the approach is completely reversed: from an ”offline-to-online” paradigm (Facebook) to an ”online-to-offline” paradigm. This means that dating application users can find new real life connection thanks to a first interaction on a digital platform (online-to-offline approach will be better explained in the second section). Moreover, taking into consideration the relevance of the content posted in the user experience, dating applications are on the opposite part of the spec-trum respect to content-based social networks. The information users share

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is in most cases minimized to basic information such as gender, age, name or nickname and some pictures. The main drawbacks of these platforms are: the only purpose of a real-life meeting is to find a romantic partner and the poor information available on the user profiles enhances the ”fear-of-unknown” effect. Moreover, the lack of trust among the users that negatively affect the sharing of real life moments. Even though, statistics confirm the growing popularity of this new trend: according to ”Statista”, 84% of young people from 18 to 29 has used dating applications to find a romantic partner. Between these two segments, it is possible to place the category of EBSNs. Differently from content-based social networks, in EBSNs the only content that users can share is ”social event” content. Furthermore, it has to re-spect a standard format in order to be accessible to all interested users. On the other hand, similarly to dating applications, the activity of a EBSN is not limited to digital interactions but the user’s scope is to share real-life moments with other people. Moreover, if the main purpose of meetings on dating application converges towards a romantic appointment, in EBSN the purpose can be whatever. According to these metrics (type of content shared and real life interaction purpose), it is clear that EBSN can be placed in a middle region between CBSNs and dating applications.

Figure 1.2: Segmentation of SNs according to how they satisfy the need of getting connected (Source: our elaboration)

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Fig. 1.2 presents a qualitative segmentation of SNs world according to how the need of getting connected is satisfied. According to our analysis, the need of getting connected can be satisfied by SNs in two ways: enhancing real life meeting opportunities (x-axis) or through the exchange of content online. The latter can be only text (Twitter), only image and video (Insta-gram), whatever (Facebook and LinkedIn), related only to events (Meetup and Local), or limited to basic information such as few profile photos, age, and a brief person description (Tinder, Loovo, Happn). As a proxy of the vertical axis, it is possible to use the variety of the content possible to share or the frequency of the data shared (the two metrics are proportional). On the horizontal axis the capacity of any SN to enhance real-life meetings is evaluated . Accordingly, three main clusters of SNs have been identified: CBSN, EBSN, Dating Applications. The first kind of platforms prioritize the online connections among users. These solutions are content-oriented and have based their businesses on the maximization of online shared con-tents such as photo, video, and text. The second and the third focus on the building of online connections among users in order to bring them into real life relationships. In particular, on EBSNs the content shared is related only to social events and the real life connections are oriented to group of people rather than only to couples. However, since the purpose of the meet-ing in EBSN is very wide, there is too much dispersion between the events available and the desire of the user. Therefore, the orientation to real life meetings is more limited than in dating applications. Finally, since dating apps focus only on romantic appointments there is less dispersion than in EBSNs and, for this reason their capacity to enhance real life connections is actually much higher.

We believe that EBSN is the most promising typology of social network. In fact, an EBSN minimizes the ”vanity effect” reducing the typology of the content posted only to social events/activities and maximizes the real-life meeting opportunities being open by default to every kind of activity and not only to romantic appointments. To improve the potentiality of EBSNs, it is critical to lessen the dispersion between user and event. This is the objective of our proposal: the development of a user-centred EBSN that focuses on customization of user experience thanks to the development of dynamic user profiling, real time event management, and a well structured recommendation system able to connect the two entities (user and social event).

EBSN can open people to new meetings enhancing the probability to estab-lish durable real-life relationships that can improve the quality of life. Have you ever wondered why women usually live longer than man?

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Figure 1.3: life expectancy at birth for males and females 1981 to 2016 in UK (Source: Office for National Statistics life expectancy data for single years. Data for 2016 are provisional and produced by Public Health England)

According to the psychologist Susan Pinker, women live longer because they tend to prioritize face-to-face relationships over their lifespan (Fig. 1.3). In fact, building and enhancing in-person interactions bolsters the immunity system [S.Pinker, 2017].

A research performed by the Harvard University provides another interest-ing point of view about the importance of relationships. This is maybe the longest study of adult life that has ever been done. It lasts for 75 years and the lives of 724 men and women have been tracked, year after year, ask-ing about their work, their home lives, their health. What they found out is simply astonishing: social connections are essential for people, and that loneliness kills. In fact, it emerged that people who are more socially con-nected to family, to friends, to community, are happier, physically healthier, and they live longer than people who are less well connected. Moreover, it is not just the quantity of friends that matters, but moreover the quality of the closest relationships. [R.Waldinger, 2016].

In [J. Holt-Lunstad, 2015], the authors studied what are the factors that most reduce the chances of people dying. As shown in Fig. 1.4 ”social inte-gration” and ”close relationships” are the most important ones: people with stronger social relationships have a 50% higher likelihood of survival than those with weaker social relationships. Relationship network is a fundamen-tal factor in our life that is strictly related to the quality and to the length

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of our life.

Figure 1.4: correlation between length of life and contingent factors (Source: Julianne Holt-Lunstad, 2015)

Furthermore, there is a substantial difference between real-life relationships and the digital relationships established on SNs. According to Elizabeth Redcay, ”simply making eye contact with somebody, shaking hands, giving a high-five releases a whole cascade of neurotransmitters (release of oxytocin, release of dopamine and reduction of cortisol level), and like a vaccine, they protect us now in the present and well into the future”. All these benefits of face-to-face contacts are not achievable via Social Media.

The real life social network of friend is a key protective factor for our health. The social support acts ”cushioning” what is the physical and psychologi-cal impact of stress: the people who can count on a stable and satisfying network of relationships are also those that get sick less, experience less psy-chological discomfort and have a better psypsy-chological attitude. Moreover, in the last year psychologists and neuro-scientists have started analyzing the relationship between the brain’s dimension and capability with the circle of friend’s with.

In [E. Redcay, 2017], Elizabeth Redcay stated that: ”our ability to seek out social partners, flexibly navigate and learn from social interactions, and de-velop social relationships is critically important for our social and cognitive

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development and for our mental and physical health”. The social-interactive context fundamentally alters cognitive and neural processing.

Our social connections and our influence circle determines the quality of our lives, has an impact on the length of our lives, and affects our brain development, our thoughts, and habits. But who determines our network of friends? Among all the new and old social networks that we can use, is there anyone capable to help us in maximizing the return of social relationships, the most decisive aspect of our life? We think the answer is no. That is why we decided to develop this project.

Our thesis has been developed starting from these consideration. It is clear why it is fundamental to develop an innovative EBSN. It has to capture the attention of young generations since those are more affected by the collateral effects of the technology trends.

Our thesis can be seen as the first comprehensive guide to develop an Event Based Social Network (EBSN). EBSN is at the intersection of three main topics: events management, user profiling, and recommendation systems. Chapters two, three, and four are dedicated to the analysis of the main re-searches in these topics. Chapter five describes our innovative proposal of EBSN. Finally, in chapter six it is presented a qualitative evaluation of our proposal considering the specific growth strategy that a two-side platform needs to have in order to be successful.

The second chapter provides a detailed analysis of the state of art regard-ing the essential arguments to understand events in the digital era. More specifically, several classifications of events are presented; then, different approaches found in the scientific literature to extract, index, classify and detect physical events from multimedia are described. At this point, we introduce the criteria necessary to build a unified event model and what are the advantages related with it. The chapter shows also the different on-tologies developed so far to model the event entity. Finally, the main event providers available on the market are analyzed with a particular attention on how APIs can be used in order to retrieve event data.

Since EBSNs are subgroup of SNs, we devoted the third chapter to the analysis of User Profiling (UP) within SNs. Here, the main parts of the user profiling task have been analyzed: modelling, construction and man-agement. The third chapter answers the following questions: what are the principal information that can be used to develop a user profile?; how the information seeking problem and the cold-start problem can be solved?; how can a user profile be modelled?; how is it possible to build a user profile (im-plicit, ex(im-plicit, and hybrid approach)? And still: what are the main issues

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related with the management of a user profile? What are the experimented approaches providing dynamicity in the profiling process? In particular, thanks to the advancement in technology, in the last years there is high im-portance on the dynamic aspect of user profiling. It is important to consider that we, as people, are in constant change and therefore intelligent applica-tion services have to capture this dynamicity to be eligible for a big market audience. The ideal EBSN have the capacity to dynamically upload user profiles according to its preferences changes in order to provide tailored user experiences.

Chapter four analyzes the EBSN platforms, their properties and assesses also the Recommendation System (RS) topic with a specif focus on the event recommendation task. The first part of the chapter is dedicated to a deep analysis of the properties of EBSN. It is stated that they are char-acterized by the presence of the offline graph along with the online one (as every social network). Then, since EBSNs, in their essential aspect, are tailored social event recommendation services, the main types of recom-menders have been analyzed. Among the possible classifications, the most important one divides RSs into Content Based (CB) and collaborative Fil-tering (CF) families. Furthermore, the following topic are explained in this section: the metrics adopted by recommenders, the methods to evaluate them and the main issues with the implementation of RSs. Furthermore, in the last section, it has been pointed out that event recommendation task is more challenging than a more common book/film recommendation: events are unique entities characterized by low frequency data sharing. Therefore, the principal approaches to solve the event recommendation task developed in the academic researches are introduced.

The innovative solution is described in chapter five. Thanks to the model and solution studied in the state of art chapters, we show our model of EBSN explaining our innovative contribution from a design point of view. We introduced two main innovations in this field.

The first contribution tackles the problem of low data frequency that charac-terize every EBSN. We try to solve this problem leveraging on two functions of our model. The first function is a technical one: it is based on the im-plementation of APIs connection with other event’s platform in order to populate our system. The second is a user design oriented function: the outsourced events have to be promoted by users. In the moment a user decides to promote an event, the event becomes an ”activity” and the user becomes the ”host” of that activity. Therefore, each event can be promoted by many users that can specify the target audience of that ”activity” further enhancing both the personalization and the frequency of data sharing. The second main function proposed regards the post activity phase. Hav-ing observed that, the most important feature in the prediction of potential

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participant to an event is the ”network feature”, our proposal of EBSN is characterized by a tailored creation of the online graph (the graph compre-hending all the social connections) for the user. In this way, every user can specify the people he/she wants to add to its connections in order to improve both the user experience and the predictive results of the reccomendation system. The sum of these two contributions is resumed in the label: user-centred EBSN (the first model proposed according to our knowledge). To summarize, the user-centred EBSN model proposed is based on the fol-lowing essential features:

• a user can directly create its own desired activities from scratch or from the customization of a public event coming from external sources; • a user benefits from a dynamic user profiling that allows him/her to

have personalized experience within the digital platform;

• a user will be able to manage its own online social graph after the activity has taken place.

In this way, we have redesigned the approach to SNs to allow people to change and innovate their own social life through a platform that leverages on a recommendation system that takes into account the specific dynamic profile of a person, its personalized network of friends and the particular characteristics of a social event. The fifth chapter is divided in three sec-tions. A detailed description of all the new features introduced in our model is presented in section one. In section two, the main functions of the user experience are described. The section is divided into three phases: setting phase, pre-activity phase and post-activity phase. Section three finally de-scribes the prototype developed to test the core functionalities of the model. In chapter six a strategic selection of the early adopters and a validation of the proposal is elaborated. Starting from the presentation of a hypothetical growth strategy for the proposed platform, we identified the main charac-teristics describing the desired future early adopters which are essential for the growth and the evolution of the solution. Moreover, in order to validate the proposal one survey has been launched. The results of the analysis are finally summarized and described.

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Chapter 2

Social event analysis in the

digital era

“An event is a special opportunity for an experience of fun or pleasure, social or cultural, outside of routine and everyday experiences.”

D. Getz, 2005

“Everything that is different from a normal day of life.”

J. Goldblatt, 2001

An event is a unique experience, action or an occurrence with a precise public or private scope with a limited duration over time going on at a specific location and involving two or more people. In the majority of the cases, the event is sponsored by an individual or an organization that will be defined as host.

Events have is limited duration. The key element of an event is its limited extension in time. Events are different to entities: events unfold over time while entities are in time. Another important consideration is the fact that events are unique: even if they were repeated over time, there would not remain the same characteristics. In fact, not only the event’s date surely changes but also participants, place, and duration may vary.

In this chapter we are going to expose a variegate literature review about events in the digital era. Starting from possible classifications of events in real life, we move to analyze how these events are stored, modeled and detected in digital applications. Finally we discuss about the most known event platforms available in the market with a specific focus on how events data can be integrated among different applications with APIs (Application Programming Interface).

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2.1 Event classifications

Since the above definitions of event are very broad, it is possible to provide many classifications. According to Getz [D. Getz, 2008] a useful framework to clearly identify an event is through the following clusters:

2. Cultural celebrations 2. Political/civil events

3. Artistic and entertainment events 4. Business and commercial events 5. Scientific events

6. Sport competitions 7. Recreational events 8. Private events

Figure 2.1: Typology of planned events (Source: Getz 2005)

Public and private social events. Events are often in relation with arts or with hobbies and leisure activities. Social events can be divided in public social events and private social events. Public events are characterized by the presence of a large audience of people. Usually the host is a professional event organization that spread the event all over the available marketing channels. The main objective is related to maximize the number of par-ticipants who usually have to pay to assist the exhibition. Private events, instead, can be organized by everyone (alone or in group) and the aim is

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usually not related to the maximization of profit, but to enhance the social life’s quality of participants. Most of the times, the people involved in pri-vate events are friends or friends-of-a-friend of the host.

Social events dimension. Taking into account the events dimension fac-tor there are four typology of events: mega events (biggest events with an international target); hallmarks events (related to a specific location and its characteristics); major events (when there is a professional activity going on and the attraction of local interest); minor events (local or ”community” events related to cultural, musical and sporting events). The latter category of events are the most frequent ones and are devoted to the enhancement of the social life of people. Although they are small in size, they provide social benefits enhancing the sense of belonging and the satisfaction of a community.

Social events typology. Another important and useful classification is re-lated to the typology of the events. There are several possible categories to which social events belong. The most general typology classification involv-ing public events distinvolv-inguishes them among sport events, cultural events, and business events. Public sport events are characterized by the capacity to attract a big audience, the ability to ensure media interest. Moreover, their impact on the economy of a place is substantially relevant for public administrative strategies. Public cultural events encompass arts, exhibi-tions, music, concerts, theatre and gastronomic events. Finally, conferences, conventions, workshops, and corporate events belongs to the category of business events.

Other possible classifications take into consideration duration, target at-tracted, media’s attention coverage, scope.

2.2 Events within Social Network systems

In the following section, the focus is on the approaches found in the scien-tific literature to extract, indexing, classify and detect physical events from multimedia data.

Before to enter in this discussion, it is useful to specify that the term ”event” is used with two different meanings within the scientific literature related to Social Network systems. Indeed, considering the relationship between events and time, an event can be classified as discrete or continuous. Dis-crete events are those ones that happen as instantaneous incidents or state changes in an IT system. In easier words, if something changes within a

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system, that happening is called ”discrete event” within that domain. Dis-crete events are called also system events. Instead, continuous events are activities with experiential relevance to people and that possess an extension in time and therefore are called also physical events.

The focus is placed on continuous or physical events. With the rapid devel-opment of Internet, there are more and more SNs (Social Network systems such as: Facebook, Flickr, YouTube, and Twitter) for people to capture and share social media data online. As a result, a popular event that is hap-pening around us and around the world can spread very fast along different channels. Moreover, within the SNs there are a high amount of events with multimodality. Event can be expressed as images, videos, and text. Events are currently receiving attention in the experiential computing con-text, which aims to develop multimedia applications that explicitly address users’ event centricity. In fact, one of the main literature challenge nowadays is related to the establishment of a correspondence between social network users and the large amount of events available online. The realm of phys-ical events taken into account in the researches of the literature analyzed does not encompass only social events but also natural events and historical events.

Many papers leverage on the use of SNs, mainly Twitter, to detect event nat-ural crisis such as tsunami or earthquake. Researches focused on these kind of physical events can be found in the following papers: [E. Middleton, 2014], [S. Pezanowski, 2017], [B. Perinici, 2018]. The above cited papers, are not included in the classifications provided in the precedent section. However, being focused on the event detection task, they provide interesting solutions for the event detection task on SNs leveraging on geotag analysis, natural language processing (NLP), and image extraction techniques.

Another event’s category of interest regards the historical events. Historical events are interesting because they can be considered as linking hub of many entities. So far, there are two possible approaches related to the retrieving of this kind of events from Wikipedia: extracting events from the main article text and the creation of events from the article itself. The first approach is developed in the paper [A. Bhole, 2007] where the authors apply NLP (Natural Languages Processing) methods and semantic parsing to identify phrases containing a date attribute and relate them to the typed article en-tity. The second approach is presented in the research of [F. Luciano, 2012] where the focus is on the creation of historical events from Wikipedia arti-cles. Here, it has been used a Web API, a SPARQL endpoint and a Linked Data Interface. Moreover, the data retrieved are modelled using the LODE ontology.

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Social events is the main topic of this chapter. Social events are the most popular category of events analyzed in the recent years within SNs. The massive amount of available data on those platforms raises the demand for efficient indexing and retrieval methods. A social event can be defined as being planned by people and attended by people [T. Reuter, 2013]. An important prerequisite for social event analysis is the distinction between event-related content and content that is not related to an event from a given stream of media (relevance detection task).

A study conducted by H. Becker et al. [H. Becker, 2015] focuses on identi-fying social events from social media documents. The approach followed by the authors was to define appropriate document similarity metrics to enable online clustering of media to events taking as input both textual and non-textual features (images). The exploration of similarity metrics for social media documents has been done on a Flickr event data sets. Therefore, the authors have considered a set of social media documents where each docu-ment was associated with an (unknown) event.

After the selection of event-relevant content, social event type classification or event type classification can be done. In [N. Imran, 2009], the authors present a purely image-based method to classify images into events like ”wed-ding”, ”road trip”, and ”ball-game” (Fig. 2.2). Their goal was to classify different event categories based on the visual content of a group of photos that constitute the event.

Figure 2.2: Image examples of 3 events selected from a Dataset of 3453 photos (Source: Imran 2009)

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To relate images to the same event, the authors have been used the intrinsic correlation of the features extracted from the images. The main challenged faced is that features from individual images are often noisy and not all of them represent the distinguishing characteristics of an event. Page rank is used for selecting the most important features and Support Vector Machines (SVMs) finally predict the event type (Fig. 2.3).

Figure 2.3: The flow diagram of the training phase of the proposed method (Source: Imran 2009)

The study of Bossardi [L. Bossard, 2013] proposes an approach for the clas-sification of events from multiple images in personal photo collections where only event-related images are considered. The authors exploited tempo-ral constraints for event classification taking into account the time gap between images to estimate the probability to change state. In the work [D. Schopfhauser, 2016], it is provided an extensive study of textual, visual, as well as multi-modal representations for social event classification. The data set used contains 57.165 images from Instagram with contextual meta-data (title, a number of tags, the name of the uploading user, date and time of capturing, and partly geographic coordinates). The data set con-tains images from eight event classes and an additional (much larger) set of non-event-related images (Fig. 2.4).

In [B. Huet, 2013] the authors assessed the main problem related to event based social media analysis. They were interested in both the identification and the classification of events. Moreover, they wanted to model the clas-sified events with their relation with multimedia files. To do so, their focus was on the discovery of the most important features of an events that have to be captured to associate real social events to social media data (image, text, video)?”. The authors found out that the most representative features are tags, location and time. Furthermore, they exposed an approach to deal with missing and erroneous meta data.

To summarize, the major challenges in the context of social event identifica-tion and classificaidentifica-tion on social media are: the high degree of heterogeneity of the visual media content showing social events and the incompleteness and ambiguity of meta data generated by users. Therefore, in the following sections we are going to discuss the structural necessity of a unified model and ontology framework for events management within SNs.

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Figure 2.4: Examples from three different event classes and of images that do not represent an event (Source: Schopfhauser 2016)

2.3 Unified event model

In this section we describe two frameworks of unified event model that ac-cording to the corresponding authors, would provide a common foundation for event modelling into different applications. Many events applications use proprietary event models defined to suit their immediate needs. A com-mon event model would be able to offer many advantages both for the users and for the developers of new applications. Indeed, a common event model would enhance the application integration and facilitate the implementation of infrastructure and tools for event management.

According to [U. Westermann, 2007] there are six elementary aspects that have to be considered for event description (Fig. 2.5).

1) Temporal aspect: events are in time, therefore it is not surprising that an event’s temporal aspect is essential for any event digital application. Ap-plications might be considered in both an absolute manner and a relative one. In the first case it has to be defined the start time, the duration and the date; in the scond case information that are relative respect to other facts or situations have to be included.

2) Spatial aspect: a common multimedia event model has to support the spatial aspect in an event’s description. Different ways of capturing the spa-tial aspect are possible. Analogously to the temporal aspects, events digital platform might want to express an event’s location not only in an absolute

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Figure 2.5: Basic aspects of event description (Source: Westermann 2007)

manner but also relatively to other events’ locations.

3) Informational aspect: information about the events that happen have to be provided by a unified event model. It is possible to taxonomically orga-nize event types. The important informational information have to include: the actors, the participants and other entities involved with their roles. It might also involve further characteristics describing the event. Depending on the application, different methods might be adequate to capture actors and entities involved in an event.

4) Experiential aspect: this aspect regards the possibility to access to various types of media documenting events. Indeed, a common event model that aims to serve as a base model for multimedia applications must show media awareness and let events refer to such media. Every kind of media should be ideally supported by a unified event model. It has to be possible to range from traditional discrete media such as images to and continuous media such as video. Moreover, more complex media such as multimedia

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presentations can be included.

Structural aspect: From a structural point of view, every event can be seen as a sum of many sub-events. Thus, the inspection of many sub-events that arise as part of a more complex event may offer important insights for event classification and analysis. Therefore, a suitable common multimedia event model should consequently support the representation of event com-position.

Causal aspect: an event is not an isolated entity but it is always in relation with other happenings. Therefore, the cause of an event is also an important aspect to be considered. A common multimedia event model should offer means to express causality. This would enhance the explicit representation of chains of causal events for individual events. The relationships between events are important as well.

In the work [X. Wang, 2008], it is possible to find another perspective for the implementation of a unified event model. In this case the basic features considered are the standard facets Who, Where, When, Why, What plus the How. Compared to the previous unified event model, the principal aspects proposed here present some differences. The temporal and the spatial aspect are labelled as ”When” and ”Where” and remain the same. The following features are instead slightly different since they focus on different things. Who - Subject: this field refers to the participants and the actors involved in a specific social event.

What - Actions and activities: the field describes the action that are ongoing in the event or in the media clip. It is answering the question what is happening therefore implying the description of actions going on.

Why - Event Context : this field captures the reason why an event hap-pened. It is easy to understand that to answer to this question it is necessary to comprehend more subsequent event in the analysis. An overview of the context is necessary.

How - Event Dynamics : the ”how” answers a slightly different question compared to the previuos ones. This field is useful to understand the dy-namics underling a single event. The ”Why” and the ”How”, taken together, are similar to the causal aspect of the previous model.

According to [U. Westermann, 2007], a unified event model is fundamen-tal for events management within digifundamen-tal platforms because it offers several advantages. The main benefits of a unified event model regard both

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tech-nical aspects and user experience features. They can be summarized in the following list:

• reduction of development efforts and realization of gains through reuse; • implementation of unified means for indexing different media types

from different applications;

• creation of common event management infrastructures procedures that can include: event storage and retrieval [D. Pack, 2014], event detec-tion systems generating events from media or data streams they ob-serve [A. Gupta, 2005], event inference systems deducing events from occurrence of others [A. Demers, 2005], and systems for event propa-gation between applications [S.C. Boll, 2003];

• possibility to represent events in an homogeneous way coming from different applications and promotion of integration of events digital platforms;

• providing users of data about interlinked events from different appli-cations, enhancing the insights that they could not obtain from one application alone;

• promotion of event-centred content easily accessible by users that re-quire information about events; this enhance the experience of appli-cations users since they often tend to organize memories around events attended;

• creation of specialized tools that users can use to explore, visualize, and experience the course of events they have participated.

Finally, citing Westermann: ” a common event model should provide refer-encing schemes that permit events to be related to entities and concepts from a broad variety of knowledge sources (such as external databases, knowledge bases, ontology, and taxonomies) to establish bridges for multimedia appli-cations to domain knowledge.”

2.4 Event ontology

”An ontology is an explicit specification of a conceptualization”. (T. Gruber) The term ontology has long been used in the field of philosophy to indicate the study of what it does exist. In knowledge-based systems it is adopted to understand what can be represented. Ontologys aim is to define the com-mon vocabulary in which information can be represented and to support

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the share and reuse of formally represented knowledge among AI (Artificial Intelligence) systems [T.Gruber, 1993].

More specifically, we refer to ontology as a type of data model able to rep-resent sets of concepts within a domain. Its main advantage is its ability to perform inference on the objects of that domain. For the specific do-main of event, an ontology helps also the process of classifying. Indeed having a greater knowledge of social events features may bring advantages in structuring a better data model to obtain enhanced results in social event classification. An ontology is devoted to the description of things from a structural point of view. The basic object that has to be described is called ”Individual”. Therefore, in an ontology domain are modelled:

• Classes: sets, collections or types of objects;

• Attributes: properties, characteristics, or parameters that objects may have to share;

• Relationships: between objects.

As we have stated in the above section, describing events in a unique way what is an event is not an easy task. Despite the large presence of events in multimedia, a common event model for capturing events has not yet emerged. Moreover, the Linked Data Organization (LDO), which has the purpose to find standard model to describe entities, has not yet emerged with a specific solution for the entity events.

Although the digital evolution has made possible the creation or the pro-motion of social events through social media platform, it is still difficult to know what is happening around a location. With the massive quantity of information created in these systems, finding an event is challenging because sometimes the data is ambiguous or incomplete. As said before, one of the main challenges in finding social event involves the incompleteness and am-biguity of metadata created by users [M. Rodrigues, 2018]. The issue arises because the singles communities interested in describing their specific re-sources define the semantics of metadata relevant to their needs.

The effective use of metadata requires that they come from established conventions for semantics, syntax and structure. Resource Description Framework (RDF) is the basic tool for coding, exchanging and the reuse of structured metadata, and allows interoperability between applications that are exchange machine-understandable information on the Web. Although RDF was invented by the W3C (World Wide Web community) in 1999 for the evolution of the Semantic Web, its ability to model disparate, abstract

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concepts has also led to its increasing use in knowledge management appli-cations unrelated to Semantic Web. In the context of Semantic web, RDF defines a resource as any object that it is uniquely identifiable by means of a Uniform Resource Identifier (URI). Instead, in the domain of knowledge management application a resource can be any entity, which has properties and attributes.

The RDF data model, which allows representing RDF statements in a way syntactically neutral, it is very simple and is based on three types of objects:

• Resource: anything described by an RDF expression is called a re-source. It can be a Web page, or a part of it, or a XML element. A resource can also be an entire collection of web pages, or even an ob-ject not directly accessible via the Web (ex: a book, a painting, etc.). The resources are always identified by a URI, possibly with an anchor id.

• Property: a property is a specific aspect, a characteristic, an at-tribute, or a relationship used to describe a resource. Every property has a specific meaning, defining the permissible values, types of re-sources that he can describe, and its relationships with other proper-ties. The property associated with resources are identified by a name, and assume values.

• Statement: a statement for the specific resource is a resource, with a property distinct from a name, and a value of the property. A state-ment ca be seen also as a triple composed of a subject (resource), a predicate (property) and an object (propertys value). Triples are database normalization taken to a logical extreme.

To share the RDF vocabulary, the W3C developed the RDFS (RDF Schema) defined from the W3C as a declarative representation language influenced by ideas from knowledge representation. RDFS defines some classes that repre-sent the concept of subjects, objects, predicates. This means that is possible to start making statements about classes of thing, and types of relationship. In the Semantic Web representation provided by Berners-Lee, the architec-tural component placed above the RDF layer is the Ontology Vocabulary (ontological level), understood as the container that defines in a formal way the relations between the terms. Ontologies add semantics to the schema specifying far more about properties and classes. Another useful feature that ontologies add is the ability to say two things are the same; this is very helpful for joining up data expressed in different schemes. Thanks to RDFS and Ontology, it is possible to joining up data from multiple sources and

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sites: this is the main purpose of Linked Data organization. In the following section, the aim is to go deeper in the understanding of the different ontolo-gies available to describe events. It is important to note that all the different description models have been created to serve different purposes. To the ex-tent of our knowledge, the most relevant ontologies describing events are:

• CIDOC CRM: it is a high level ontology developed to model library and archive information. It supports data transformation, mediation, and merging. The events that are described can be both historical events (elections, wars, manifestations) and sub-events happened dur-ing this major events [N. Crofts, 1998].

• ABC Ontology: this model provides the notional for the development of specific domain oriented ontologies. It comprehends many basic entities and relationships that are common across ontologies such as time, agency, places, and concepts. If at the first layer of the model there are entities, at the second one three categories are identified: temporality, actuality, and abstraction. Then, temporality has other three sub-classes: situation, event, and action. Because of this par-ticular structure of the model, it is very useful to conceptualize time-dependent properties of (possibly multiple) entities [C. Lagoze, 2001]. • Event Ontology (Fig. 2.6): it has been developed by the Centre for Digital Music to describe music-related events. Anyhow, there is noth-ing specific about music and it is actually the most used in the Linked Data community [Y. Raimond, 2007].

• Events ML-G2: it has been developed by the International Press Telecommunications Council (IPTC) to describe information about planned, past and breaking events reported in the news. These events regard for example elections, meetings or sports competitions.

• DOLCE + DnS Ultralite (DUL): it is an upper ontology. It is a com-bination and simplification of the DOLCE (descriptive ontology for linguistic and cognitive engineering) foundational ontology. The main contribution provided by this upper ontology is the answer provided to the discrimination of events respect to other categories. Indeed, there are standard differences between category event and category object. The former have a limited duration over time and therefore is defined as an entity that exists in time, the latter just exist without an evident relation with time and it is defined as an entity that exists in space. • F Event Model: it has been built on top of DUL [A. Scherp, 2009].

The F model follows the distinction underlined by the DUL ontology between event and object category. The main contribution regards the

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Figure 2.6: Event ontology (Source: Raimond 2007)

introduction of additional properties and classes for modeling partici-pation in events, as well as part-hood relations, causal relations, and correlations between events.

• LODE: it has been developed in the study [R. Shaw, 2010]. It is useful because the main purpose of the authors was not to provide another ontology per se but to define an ”Interlingua” model that solves an interoperability problem. In fact, it compares existing event models, looking at the different choices they make of how to represent events. • LODSE: it is based on the previuos framework (LODE). It is the

newest model for social events developed by [M. Rodrigues, 2018]. For the developing of our project, the LODSE (Linking Open Descriptions of Social Events) ontology has been take as a main reference. Therefore, we think useful to report in detail its characteristics. It adds important prop-erties to the event entity enhancing the classification of events. Moreover, since it is based on the LODE framework, it can be used by different appli-cations and allows a better integration of data among them. The domains covered by the LODSE regard social events. In particular: music, sports, performing arts, conferences, among other types of events.

LODSE includes all the information necessary to answer to the following questions:

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• What event is it?

• What is the name of the event? • Who is the artist?

• Who is the organizer? • Where will the event occur? • What time is the event? • What kind of event is it?

The most innovative aspect of the LODSE is that events are no more catego-rized by categories (business, sports, nightlife, food and drink, leisure) but by tags. The tags can be seen like keyword that specify the characteristics of the event. In this way, social event classification can be performed with higher efficiency and the user experience is improved because the searching and the recommending is faster and more reliable.

In order to understand the application of tags to classify a social event, next is presented an example of a concert of Ultimo at San Siro stadium. The tags used to classify the event are:

Event: Concert of Ultimo at San Siro stadium; Tags: music, pop, young, romantic, piano.

In the following part, the classes that are included in the ontology are re-ported:

Event: this is the main class and incorporates all the basic information about the event (name, description, price, others).

Involved: this class describes the people that are involved in the event. It answers the questions Who is the artist? and Who is the organizer?

• Artist: description the artist of the event; it is a subclass of the class Involved;

• Organization: description of the organizer of the event; it is a subclass of the class Involved;

Date: the fundamental time aspect is described by this class;

• start-Date: a subclass that answers to the question: ”When the event starts?”;

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• end-Date: a subclass that answers to the question: ”When the event ends?”;

Venue: the fundamental location aspect is described by this class;

• City: a subclass that answers to the question: ”In which city the event takes place?”;

• Country: a subclass that answers to the question: ”In which country the event takes place?”

Taxonomy: this is the class that enhances the classification and the rec-ommendation performances of any event provider. This class answers the question What kind of event is it?

• Tag: the names of the tags are described in this sub-class;

• Category: the name of the category are described in this sub-class.

Figure 2.7: LODE and LODSE ontology compared (Source: Rodrigues 2018)

Figure 2.7 shows how LODSE differs from LODE ontology. In particular, it is important to note that classes Category, Taxonomy and Tag are new and were created for the specific purposes of the LODSE ontology.

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Finally, it has to be considered that the properties of the classes described in the LODSE ontology were chosen also accordingly to specific analysis made of several event APIs (Application Programming Interface) such as Face-book, Eventful, Eventbrite and Meetup to perceive the common properties between these services. Anyhow, even if the LODSE encompasses the most relevant information to characterize events, the causal and the structural aspects (event dynamics) are not included in this model.

2.5 Event-based platforms

As we have seen in the previous chapters, events are characterized by having specific locations and times assigned, as well as a description comprehensive of all the other important information. Nowadays, in the digital era, events are spread all over the web. Events’ organizers want to maximize number of participants, while participants want to join the events that match the most with their interests, attitudes and as a delight with the other participants. Therefore, many event-based digital platforms arose in the market with the scope to connect these two sides. People use these applications to buy tick-ets or to get informed about upcoming initiatives in their cities. Of course, every platform has a different purpose, maintains a particular structure, and has a specific layout to optimize its user experience.

The users’ need for future event discovery is growing as the available events on the web are getting vaster. Since events are presented on a wide vari-ety of sources in different formats it is not easy to find relevant events if a person wants to find an interesting activity near a particular location and date-time or in another city for a specific period of the year. Several so-lutions to this problem are available, but with limited exploratory options and some limitations that did not bring yet the entire event-based platforms environment to a clear standard for online event creation, management and analysis. This chapter investigates some online event sources available to-day, to what extent do they use relevant meta data and how accessible they are. Therefore, it is presented a description and analysis of the APIs used to connect these platforms. It is also worth mentioning that because of le-gal issues, some providers only allow personal use of content available on their platforms and prohibit usage of data mining tools if not agreed upon their Terms of Service (ToS). On the other hand, there are several platforms that use an open data approach permitting third-parties developers to anal-yses the relevant users and events data produced within their web-based environments.

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2.5.1 Application Programming Interface

If we consider these event-based digital platforms as sources of web-based events, it is necessary to introduce the concept of API that we already briefly introduced in previous chapters.

”In computer programming, an application programming interface (API) is a set of subroutine definitions, protocols, and tools for building application software. In general terms, it is a set of clearly defined methods of commu-nication between various software components” [Wikipedia, 2018].

In general, APIs allow user to make ”request” to a web server which ac-cesses the application database (with the users’ data) and returns it to the requester in a ”response”. The response can return in different formats: JSON and XML among others. Some APIs, like the ones of Reddit and Spotify, are designed to expand the visibility of their own application by making their data available to users in other software environments and en-abling external developers to build products that are in some way reliant on their own standards.

Nowadays APIs are becoming a milestone for many digital businesses that rose up during the last years. It’s really common to find integration between SNs and new mobile applications. But in order to provide this integration to customers, two different web applications must be aligned and agreeing about the retrieving and usage of users’ data. With this scope, two different types of APIs appeared in the market.

Private API

A private API can be also called ”closed API”. These are indeed closed to the environment out of the company, but in many cases the closed data could be reached by third-parties developers through the utilization of a to-ken that permit the data retrieving for what concern data related to a user or a group of users in the system (i.e. private Facebook API). They are typically used within an organization in order to enhance the collaboration of different departments that have their own data model.

Open API

An open API allows open access to publicly data to for all developers. They allow developers that are outside of an organization’s workforce, to access back-end data that can then be reused to enhance their own applications. By the way, many digital platforms have developed open APIs during last decade in order to create an environment of different stakeholders around their data structure and enhance their roles within the digital market (Fig. 2.8). To sum up, for open APIs it is useful to underline three main aspects:

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1. they are based on an open standard; 2. they have few access restrictions;

3. data that can be retrieved is not covered by copyright, patents or other mechanisms of control.

Figure 2.8: Open API business chart (Source: Wikipedia)

2.5.2 Eventbrite

Figure 2.9: Eventbrite Logo, 2018

Eventbrite is the largest self-serviced ticket platform in the world. It is a fully customized event planning platform with intu-itive tools that help save time and increase event sales. It was founded in 2006 and is the first major player in the event market in USA. In 2018, Eventbrite went public on the New

York Stock Exchange with an IPO (Initial Public Offering) of 200 million dollar. The platform can sponsor simple as well as large and complex events. It provide to users also the possibility to get registered into the event and buy the tickets. Basically, the service allows users to browse, create, manage, and promote local events. The organizers have the ability to spread their own initiatives within the platform, with a small fee paid to Eventbrite for the promotion service. Eventbrite also offers a robust open API, which al-lows advanced users to pull event, attendance and order data into the system by integrating with a wide variety of other applications.

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Eventbrite mission is to bring the world together through live experiences and after 13 years of life, this solution has achieved a key role in paid events organization platforms. In Italy it has created a positive brand awareness and identity, in fact many Italians are using the service and have declared to be satisfied about it. According to the active users’ reviews of the appli-cation, Eventbrite is well structured, clear and useful. By the way it keeps some limitations. The event typing is not always consistent i.e. sometimes concerts are typed as visual arts or plain event. It has not any social fea-ture within the app, there are no connection among users that can use the application even without creating an account.

2.5.3 Facebook

Facebook is the largest Social Network in the world, almost everyone uses it or at least have an account. Its events feature has been around since the social network’s very early days. That is why today it represents the most used platform for event discovery in Italy and all over the world (within Facebook there are by far much more events listed than most other apps on the market). People and organizations have the opportunity to create web pages through which they can also create events. It contains a lot of small local events, but also larger ones to some extent.

Users can use the official Facebook app to check out events, or they could download the Facebook Local app, a dedicated app (available since the end of 2016) for discovering new events around a specific location and time pe-riod. Facebook Local’s home page display to users restaurants, cafes, drinks, attractions. Users see a calendar of their day’s Events, a Trending Events feed, guides to music, nightlife, art, and other happenings, and options to see everything going on certain days. A ”Discover feed” shows top sugges-tions and what’s popular with friends.

Figure 2.10: Fb Local Logo, 2018

Facebook provides a closed/private API that requires a user access token to search for specific data. Basically, third-parties de-velopers could use the Facebook APIs only to analyze the data of users that voluntar-ily give them the authorization. For what concern the events, the process it’s really easy, in fact the majority of event-based ap-plications nowadays are connected through APIs to Facebook events in order to enrich the number of events present in their

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to spread events online, but not the most beloved. Users find difficult to browse all events present on the platform and ask for a more personalized user experience. Facebook Local, as Eventbrite, represent for users a sort of event-aware platform. Most of the people use these applications to get informed about upcoming events near them in time and locations. Usually, the activities spread on these channels are paid and public events such as concerts, disco parties, workshops, and exhibitions. These events are orga-nized in most of the cases by third-parties organizations, public pages, or directly by locals.

2.5.4 Meetup

Figure 2.11: Meetup Logo, 2018

Meetup was founded in 2002 by Scott Heiferman in New York. It has been thought to find people willing to help after the September 11 attacks on the Twin Tow-ers. Today, it is the most important EBSN. It is useful to organize events and activities with people with similar interests.

In particular, Meetup is a group-centered Event-Based-Social-Network that counts almost 35 million of users. It has an in-ternational spread and in Italy its diffusion

is related mainly to the initiatives of the ”Movimento 5 Stelle”. Moreover, many technology/business oriented events and also music-oriented events are organized every day in the main city of Italy thanks to it.

Meetup is about connecting people with something in common, differently from other solutions it spreads mostly private events. While the connec-tions begin online, the real memories are made at events. Meetup events are real-life gatherings where members and organizers get together to connect, discuss, and practice activities related to their shared interests. Users can basically find groups and events based on their location and interests and then join as many as they want.

Meetup provides an open API through which it is possible to get descrip-tion, duradescrip-tion, host of event, ticket info, group hosting the event, photos, max limit of attendance, attending users, status and more. For this reason, it is often used in relevant academic studies on event recommendation. The main limitation of Meetup is that to create an event a user has to create a group before and it has to pay to create it (the fee is 15 dollar per month). This is not encouraging for people who do not have a business scope related with the event creation. In fact, it is not surprising that the majority of the events are linked to business issues, politics movements or outdoor activities

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that in the most of the case have fees to be paid from participants. This issue brings also limitation in the event search phase of the user experience.

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Chapter 3

Dynamic user profiling

”A user profile is a structured construct containing information both directly and indirectly pertaining to a user’s preferences, behavior and context.”

Riddhiman Ghosh, 2009

User profiles development it is essential to obtain knowledge about users of software applications. User profiling is at the intersection of artificial intel-ligence, data mining, social networks analysis and learning methods. Social profiling is in the domain of user profiling but is related only to social net-work services.

In this chapter, we are going to present the challenges analyzed in academic researches regarding the creation and the management of a user profile, tak-ing into account what kind of information constitutes a profile, how the user profile can be represented, how the information is retrieved, elaborated and managed. Among all the different types of application services, there will be a particular consideration for the development of user profiles in the field of social network. The majority of the cited papers in this section have been found through Google Scholar, Ieeexplore, Springer Link, Academia, Web of Science, ACM Digital Library, and Researchgate.

3.1 SNs and social profiling

Since the focus of this thesis is to build an EBSN that is a sub-category of SNs in general, we are going to analyze the concept of social profile in this domain. SNs aim to connect people. They allow individuals to construct a profile within a bounded system and develop a list of other users with whom they share a connection.

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Thanks to the fast development of these SNs a huge amount of people has started to connect each other, which motivates users to continuously share their personal contents across the social websites. This content can be also called Social Data. It includes user shared information, its network of friends, conversation history etc. All together these huge amounts of data could be used to clearly represent different aspects of peoples information, attitudes, interests, behaviours and social activities. Therefore, today, these SNs offer us both challenges and opportunities to utilize the so-called social streams to better support information search and recommendation of social connections.

Social profiling is the process through which a SN builds a user’s profile us-ing his or her publicly and voluntarily shared social data. A person’s social data refers to all the personal data that they decide to share either online or offline (gender, country, race, language, location, interest, etc...). Alto-gether, this information can construct an efficient and useful person’s social profile. A profile in a social network can be seen as a node in a graph with a multitude of edges linked to other nodes (social connections). This node is unequivocally represented by a list of attributes and by its link with other node.

The main challenge related to the implementation of social profiles is that users differ in their preferences, interests, background and goals when using applications. Discovering these differences is vital to providing users with personalized services [S. Schiaffino, 2009]. Peoples preferences and interests in fact tend to change over time, especially in scenarios where they interact customarily with a wide range of items. The most relevant classification emerged in the last years of research about attributes in social profile is the one between static and dynamic features.

• User static features: age, gender, education, etc.

• User dynamic features: interests, attitudes, locations, etc.

There is a spread in the literature researches about the dynamicity of a profile because is mainly thanks to these kind of attributes (dynamic ones) that is possible to have a deeper understanding of who a user is. A part from localization, users preference that is a specific type of opinion derived from comparative perception between two objects [S.O.Hansson, 1995] has a dynamic character as well. For instance, when a user expresses ”I dont like to read about politics. Sports news are much better”, it is possible to clearly identify his preference to sports news over politics. Since we as peo-ple are in constant change, also our preferences constantly evolves. The