2. Our path Understanding emphaty in a Twitter community - Valerio Cestrone, Simona Balbi, Agnieszka Stawinoga What empathy is ? How can be measured on Twitter ? Who is creating empathy ? Who is more empathic ?

3. What empathy is ? Understanding emphaty in a Twitter community - Valerio Cestrone, Simona Balbi, Agnieszka Stawinoga “Conceptualising and Measuring Empathy” - 2010. Authors: Gerdes, Segal, Lietz. “one individual feeling the inner experience of another” + “responding compassionately” = re-Tweeting Sentiment

4. Approaching the problem Understanding emphaty in a Twitter community - Valerio Cestrone, Simona Balbi, Agnieszka Stawinoga Getting data [ 234,255 tweets ] Twitter API – Track of EN 198 adjectives ( 99 positive, 99 negative ) ~ 90 mins No single location specified Reduced data [ 85,206 tweets ] retweet_count > 0 followers_count > 10 friends_count < 50000 only EN tweets from non protected accounts

5. Dataset Understanding emphaty in a Twitter community - Valerio Cestrone, Simona Balbi, Agnieszka Stawinoga 85206 obs. Positive 45151 ( ~ 53% ) Negative20828 ( ~ 24% ) Top 25% Tweets8502 ( 10 % ) Unique users5817 ( 20% ) Tw per user1.44

6. Visualize Understanding emphaty in a Twitter community - Valerio Cestrone, Simona Balbi, Agnieszka Stawinoga Density1.77705e-05 Vertices97927 Edges85206

7. Just saying... Understanding emphaty in a Twitter community - Valerio Cestrone, Simona Balbi, Agnieszka Stawinoga

8. Who is spreading empathy ? Understanding emphaty in a Twitter community - Valerio Cestrone, Simona Balbi, Agnieszka Stawinoga Measured in term of most retweeted

9. Who is creating empathy right now ? Understanding emphaty in a Twitter community - Valerio Cestrone, Simona Balbi, Agnieszka Stawinoga Measured in term of most retweeted during our window ( ~ 90 min )

10. Some problems... Understanding emphaty in a Twitter community - Valerio Cestrone, Simona Balbi, Agnieszka Stawinoga Top Twitter users had a disproportionate amount of influence Indegree ( = followers ) represents just user’s popularity, but is not related to other important notions of influence such as engaging audience (retweets and mentions) or neither empathy. Celebrities were better at inducing mentions from their audience. This means... We need a different approach to try to identify empathy and empathic users. “Measuring User Influence in Twitter: The Million Follower Fallacy” - 2009. Authors: Cha, Haddadi, Benevenuto, Gummadi.

11. Next level: Content network Understanding emphaty in a Twitter community - Valerio Cestrone, Simona Balbi, Agnieszka Stawinoga Words Network (positive): Density 0.059057 Vertices 914 Edges24641

12. An empathy score for tweets Understanding emphaty in a Twitter community - Valerio Cestrone, Simona Balbi, Agnieszka Stawinoga 1.Get the degree of words in the content network 2.For each single tweet check how many words of the step 1 are present in the text / tweet 3.Sum the degree of words present in the text / tweet 4.Assign this number / score to the tweet

13. Who is generating more empathy ? Who is more empathic ? Understanding emphaty in a Twitter community - Valerio Cestrone, Simona Balbi, Agnieszka Stawinoga In our set of positive tweets, first 7 out of 10 users are woman WOMEN

14. ... and negative tweets ? Understanding emphaty in a Twitter community - Valerio Cestrone, Simona Balbi, Agnieszka Stawinoga Words Network (negative): Density0.07618124 Vertices 566 Edges12181 Note: some tweets were too much rude to be shown here Hipsandbones: people r funny because no matter how much they try to make me feel bad or hate me i will never hate me Not indexed or deleted! ( Girl – 23 retweet ) Negative users seems to be more entities, news and generic accounts.

15. Future work Understanding emphaty in a Twitter community - Valerio Cestrone, Simona Balbi, Agnieszka Stawinoga 1.Test the proposed strategy to classify genere of users in Twitter 2.Explore if / how the empathy impacts longitudinally in the network 3.Control if the empathy of tweets could better explain and predict retweets 4.Explore an hybrid model of user features, content & retweet graph measures and sentiment score. 5.Optimize sentiment classification.