TY - GEN
T1 - Uncovering news-twitter reciprocity via interaction patterns
AU - Ning, Yue
AU - Muthiah, Sathappan
AU - Tandon, Ravi
AU - Ramakrishnan, Naren
N1 - Publisher Copyright:
© 2015 ACM.
PY - 2015/8/25
Y1 - 2015/8/25
N2 - In recent years, the amount of information shared (both implicit and explicit) between traditional news media and social media sources like Twitter has grown at a prolific rate. Traditional news media is dependent on social media to help identify emerging developments; social media is dependent on news media to supply information in certain categories. In this paper, we present a principled framework for understanding their symbiotic relationship, with the goal of (1) understanding the type of information flow between news articles and the Twitterverse by classifying it into four states; (2) chaining similar news articles together to form story chains and extracting interaction patterns for each story chain in terms of interaction states of news articles in the story chain, and (3) identifying major interaction patterns by clustering story chains and understanding their differences by identifying main topics of interest within such clusters.
AB - In recent years, the amount of information shared (both implicit and explicit) between traditional news media and social media sources like Twitter has grown at a prolific rate. Traditional news media is dependent on social media to help identify emerging developments; social media is dependent on news media to supply information in certain categories. In this paper, we present a principled framework for understanding their symbiotic relationship, with the goal of (1) understanding the type of information flow between news articles and the Twitterverse by classifying it into four states; (2) chaining similar news articles together to form story chains and extracting interaction patterns for each story chain in terms of interaction states of news articles in the story chain, and (3) identifying major interaction patterns by clustering story chains and understanding their differences by identifying main topics of interest within such clusters.
UR - http://www.scopus.com/inward/record.url?scp=84962542311&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84962542311&partnerID=8YFLogxK
U2 - 10.1145/2808797.2809329
DO - 10.1145/2808797.2809329
M3 - Conference contribution
AN - SCOPUS:84962542311
T3 - Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
SP - 1
EP - 8
BT - Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
A2 - Pei, Jian
A2 - Tang, Jie
A2 - Silvestri, Fabrizio
PB - Association for Computing Machinery, Inc
T2 - IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
Y2 - 25 August 2015 through 28 August 2015
ER -