TY - GEN
T1 - Dissecting twitter discussion threads with topic-aware network visualization
AU - Babvey, Pouria
AU - Lipizzi, Carlo
AU - Ramirez-Marquez, Jose Emmanuel
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Twitter is the archetype of social networks designed for information exchange and discussion among users with similar interests and passions. Twitter prompts users to disseminate a new topic and get involved in forming opinions around it. It is always of interest how and why some tweets get more attention and involve more users, especially in the case of social issues and political debates. In this paper, we use an attention-based deep learning model to extract the topics of discussion on Twitter. Then, we reconstruct the network of discussion threads to visualize a topic-aware network of discussions. The proposed method yields the amount of attention given by users to different topics over time. As a case study, we applied our analyses to discussion threads around tweets by President Donald Trump during 9 months. Such analyses help us to understand how much each topic triggers discussions, how the topics are interrelated, and how opinions form around the tweets.
AB - Twitter is the archetype of social networks designed for information exchange and discussion among users with similar interests and passions. Twitter prompts users to disseminate a new topic and get involved in forming opinions around it. It is always of interest how and why some tweets get more attention and involve more users, especially in the case of social issues and political debates. In this paper, we use an attention-based deep learning model to extract the topics of discussion on Twitter. Then, we reconstruct the network of discussion threads to visualize a topic-aware network of discussions. The proposed method yields the amount of attention given by users to different topics over time. As a case study, we applied our analyses to discussion threads around tweets by President Donald Trump during 9 months. Such analyses help us to understand how much each topic triggers discussions, how the topics are interrelated, and how opinions form around the tweets.
KW - Deep learning
KW - Network visualization
KW - Opinion mining
KW - Topic extraction
KW - Topic-aware
UR - http://www.scopus.com/inward/record.url?scp=85084741207&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85084741207&partnerID=8YFLogxK
U2 - 10.1109/CSCI49370.2019.00254
DO - 10.1109/CSCI49370.2019.00254
M3 - Conference contribution
AN - SCOPUS:85084741207
T3 - Proceedings - 6th Annual Conference on Computational Science and Computational Intelligence, CSCI 2019
SP - 1359
EP - 1364
BT - Proceedings - 6th Annual Conference on Computational Science and Computational Intelligence, CSCI 2019
T2 - 6th Annual International Conference on Computational Science and Computational Intelligence, CSCI 2019
Y2 - 5 December 2019 through 7 December 2019
ER -