TY - GEN
T1 - Analyzing social bots and their coordination during natural disasters
AU - Khaund, Tuja
AU - Al-Khateeb, Samer
AU - Tokdemir, Serpil
AU - Agarwal, Nitin
N1 - Funding Information:
Acknowledgments. This research is funded in part by the U.S. National Science Foundation (IIS-1636933, ACI-1429160, and IIS-1110868), U.S. Office of Naval Research (N00014-10-1-0091, N00014-14-1-0489, N00014-15-P-1187, N00014-16-1-2016, N00014-16-1-2412, N00014-17-1-2605, N00014-17-1-2675), U.S. Air Force Research Lab, U.S. Army Research Office (W911NF-16-1-0189), U.S. Defense Advanced Research Projects Agency (W31P4Q-17-C-0059) and the Jerry L. Maulden/Entergy Fund at the University of Arkansas at Little Rock. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding organizations. The researchers gratefully acknowledge the support.
Publisher Copyright:
© 2018, Springer International Publishing AG, part of Springer Nature.
PY - 2018
Y1 - 2018
N2 - Social bots help automate many sociotechnical behaviors such as tweeting/retweeting a message, ‘liking’ a tweet, following users, and coordinate or even compete with other bots. Social bots exist as advertising bots, entertainment bots, spam bots, and influence bots. In this research, we focus on influence bots, i.e., automated Twitter accounts that attempt to affect or influence behaviors of others. Some of these bots operate independently and autonomously for years without getting noticed or suspended. Furthermore, some of the more advanced influence social bots exhibit highly sophisticated coordination and communication patterns with complex organizational structures. This study aims to explore the role of Twitter social bots during the 2017 natural disasters and evaluate their coordination strategies for disseminating information. We collected data from Twitter during Hurricane Harvey, Hurricane Irma, Hurricane Maria, and Mexico Earthquake that occurred in 2017. This resulted in a total of over 1.2 million tweets generated by nearly 800,000 Twitter accounts. Social bots were detected in the data. Social networks of top bot and top non-bot accounts were compared to examine characteristic differences in their networks. Bot networks were further examined to identify coordination patterns. Hashtag analysis of the tweets shared by bots further helped in identifying hoaxes (such as, ‘shark swimming on freeway’) and non-relevant narratives (black lives matter, DACA, anti-Semitic narratives, Kim Jong-Un, nuclear test, etc.) that were disseminated by bots in several languages, such as French, Spanish, Arabic, Japanese, Korean, etc., besides English.
AB - Social bots help automate many sociotechnical behaviors such as tweeting/retweeting a message, ‘liking’ a tweet, following users, and coordinate or even compete with other bots. Social bots exist as advertising bots, entertainment bots, spam bots, and influence bots. In this research, we focus on influence bots, i.e., automated Twitter accounts that attempt to affect or influence behaviors of others. Some of these bots operate independently and autonomously for years without getting noticed or suspended. Furthermore, some of the more advanced influence social bots exhibit highly sophisticated coordination and communication patterns with complex organizational structures. This study aims to explore the role of Twitter social bots during the 2017 natural disasters and evaluate their coordination strategies for disseminating information. We collected data from Twitter during Hurricane Harvey, Hurricane Irma, Hurricane Maria, and Mexico Earthquake that occurred in 2017. This resulted in a total of over 1.2 million tweets generated by nearly 800,000 Twitter accounts. Social bots were detected in the data. Social networks of top bot and top non-bot accounts were compared to examine characteristic differences in their networks. Bot networks were further examined to identify coordination patterns. Hashtag analysis of the tweets shared by bots further helped in identifying hoaxes (such as, ‘shark swimming on freeway’) and non-relevant narratives (black lives matter, DACA, anti-Semitic narratives, Kim Jong-Un, nuclear test, etc.) that were disseminated by bots in several languages, such as French, Spanish, Arabic, Japanese, Korean, etc., besides English.
UR - http://www.scopus.com/inward/record.url?scp=85049773177&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049773177&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-93372-6_23
DO - 10.1007/978-3-319-93372-6_23
M3 - Conference contribution
AN - SCOPUS:85049773177
SN - 9783319933719
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 207
EP - 212
BT - Social, Cultural, and Behavioral Modeling - 11th International Conference, SBP-BRiMS 2018, Proceedings
A2 - Bisgin, Halil
A2 - Thomson, Robert
A2 - Hyder, Ayaz
A2 - Dancy, Christopher
PB - Springer Verlag
T2 - 11th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction conference and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2018
Y2 - 10 July 2018 through 13 July 2018
ER -