shown by figure 4.3, querying parameters considered are: #maltempo; #nubifragio; #pioggia; #temporale; allagato OR allagati; bassa pres- sione; bomba acqua; ciclone; diluvia OR diluvio; grandine; maltempo; nubifragio; piogge; pioggia; piove; piovendo; precipitazioni; temporale; temporali;
• ”Weather” (which talks about the weather): channel composed of all tweets containing the hashtag or simple word ”weather”.
• ”Meteo users” (weather forecasting accounts): a collection of tweets mentioning or coming from well known weather forecasting services (private and public, national and regional) such as: @3Bmeteo; @arpaER; @arpal meteomare; @centrometeo; @CentroMeteoITA; @flash meteo; @ilmeteoit;@meteoforumme; @Meteolanterna; @Meteotrentino; @MeteoTweet24; @MeteoWeb eu; @meteo fvg; @meteo toscana; @previsionimeteo; @wwwmeteoit;
As one may imagine, channels having as querying parameters ordinary words are bigger than the codified hashtag channel. The last one is by def- inition a channel collecting information produced only during high impact events and containing specific tag whereas other channels collect conversa- tions about the weather or about bad weather conditions. Figure 4.4 shows temporal distribution of messages.
4.3
Main analytics
This research has as main driver a communication perspective about the use of Twitter in Italy during particular critical situations like weather adverse conditions and emergencies. Proposed methodology to analyze data set of collected tweets is structured in four main components:
• Twitter activity and visibility metrics;
• a manual coding of users to better understand which category of actors were engaged around the codified hashtags;
• a manual coding of tweet contents to have insights about the informa- tion exchanged within codified hashtags conversations;
• a social network analysis to understand information spreading dynam- ics and central actors.
4.3 Main analytics 69
Figure 4.4: Temporal distribution of the four channels
4.3.1
Twitter activity and users metrics
”Codified Hashtags” (CH) data set was analyzed for main metrics: activity pattern over time; volume of different tweets typology over time, differentiat- ing by volumes of original tweets (original messages sent by user) and volume of retweets; volume of mentions and replies; volume of URLs in tweets; com- bined metrics like ratio native tweets/retweets.
For each data set was also evaluated the number of Active Unique Users, defined as the number of unique users sending original tweets. Visibility metrics were also calculated, in particular: number of favorite tweets; and also most retweeted users and most mentioned users. [30, 31] Relevant met- rics of the monitored Twitter channels were performed by using a dedicated R- package developed for the work and released publicly on a Github repos- itory: https://github.com/valenitna/rTwChannel4.
Main statistics were also made during four selected high impact events occurred within the monitoring period, in order to understand hashtags us- age in the different contexts in normal and emergency conditions and get insights on different communication behaviors.
Further details can be found in sections 2.3.1.
4R is a language and environment for statistical computing and graphics (https://www.r-project.org/)
4.3.2
Manual annotation of users and content
One of the aims of this work is to describe how codified hashtags have been used in the different contexts, also identifying engaged users and the kind of information exchanged on Twitter. Codified hashtags during emergencies are useful if they may function as useful channels to convey formal and informal sources of information during an emergency. Manual annotation of users and content is also a first step for developing automatic classification algorithms, which needs training data sets.
USERS ANNOTATION: For this purpose, we coded manually the data set of active unique users (users publishing original tweets). The aim was to classify users into main categories and accordingly verify their partic- ipation and active role in the conversation around the codified hashtags. To classify users we manually coded the whole set of unique authors by labeling accounts depending on their affiliation, as declared in the profile’s description available on Twitter. We considered five classes of unique users as relevant for weather related emergency management. The categories fitting the purposes of this work are: Institutions (governments and public agencies); Media (tv, radio, news and online media); Weather (weather forecasting services or fore- casting amateurs associations); Volunteers-NGO (NGOs active in the field of rescue and emergency management); Individuals (accounts of not affiliated individuals; not belonging to any of the above); BOT (computer-generated Twitter profiles that automatically repost certain tweets mentioning a user or a hashtag). By identifying classes of unique users with similar mission and role we compared their communication patterns on Twitter. Comparison of main metrics for each of the monitored codified hashtags was used to assess hashtags adoption in the different regional context and highlight different behavior.
CONTENT ANNOTATION: To identify and to measure the classes of information shared on Twitter through the codified hashtags, content of tweets was manually annotated into defined categories. A set of eleven cat- egories was considered to describe the information communicated in each tweet, paying particular attention to messages contributing to increasing situational awareness (Vieweg, 2010 [193]). Categories considered for cod- ing tweets were: Advice (how to cope with the emergency, safety precau- tions, local emergency numbers to call, advice on how to tweet; websites to follow); Warning (tweets about warnings); Hazard location (informa- tion on hazard localization or reporting about flood or weather impacts on
4.3 Main analytics 71
specific locations); Weather (information about weather conditions when directly described within the tweet); Transport Conditions (updates on road conditions; road closures; airport or public transport malfunctioning); Evacuation and Closures (message about closures/opening of public ser- vices, schools and scheduled events); Damage reports (reported damages on infrastructures or casualties); Reassurance (updates of action from first responders and volunteers’ activity on the ground); Resources (a shared resource, url, picture or video, related to weather or flood update); Com- ments (personal comments, questions, blames) and News reports (media resources shared by users). Works by Starbird et al. (2010) [177] and Hughes (2014) [92] guided the identification of categories concerning situational up- date; two more categories were added to classify media contribution and comments shared by the public broadening the general understanding of emergency impact on the population.
4.3.3
Social Network Analysis
A Social Network Analysis was performed on the data set to explore com- munication dynamics within the codified hashtags data sets. SNA is used in social media analytics for modeling communication patterns and to help identify important people in the network like influential users or opinion leaders, and relevant user communities in social media [180]. See sections 2.3.2 and 2.3.3 for further details.
To identify the important users in the network, those who show the strongest influence or act as information bridge between different groups of users main centrality measures were applied. In Social Network Analysis, centrality helps to assess the different ways a node can be important.
Centrality measures were calculated to identify the influential users in the hashtag-community during selected high-impact events. This analysis was important to assess if the codified hashtag may foster the communication among digital volunteers and institutional agencies for emergency manage- ment. Codified hashtagging could be a proactive way to interconnect official formal response procedures with the emergent information streams coming from web 2.0.
To calculate centrality metrics and visualize the network graph we used the open-source software Gephi, as further described in section 4.4.