TY - GEN
T1 - Knowledge graph analysis of Russian trolls
AU - Li, Chih Yuan
AU - Chun, Soon Ae
AU - Geller, James
N1 - Funding Information:
This work partially supported with grants from NSF CNS 1747728, NSF CNS1624503, and NRF-Korea: 2017S1A3A2066084.
Publisher Copyright:
Copyright © 2021 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved
PY - 2021
Y1 - 2021
N2 - Social media, such as Twitter, have been exploited by trolls to manipulate political discourse and spread disinformation during the 2016 US Presidential Election. Trolls are users of social media accounts created with intentions to influence the public opinion by posting or reposting messages containing misleading or inflammatory information with malicious intentions. There has been previous research that focused on troll detection using Machine Learning approaches, and troll understanding using visualizations, such as word clouds. In this paper, we focus on the content analysis of troll tweets to identify the major entities mentioned and the relationships among these entities, to understand the events and statements mentioned in Russian Troll tweets coming from the Internet Research Agency (IRA), a troll factory allegedly financed by the Russian government. We applied several NLP techniques to develop Knowledge Graphs to understand the relationships of entities, often mentioned by dispersed trolls, and thus hard to uncover. This integrated KG helped to understand the substance of Russian Trolls' influence in the election. We identified three clusters of troll tweet content: one consisted of information supporting Donald Trump, the second for exposing and attacking Hillary Clinton and her family, and the third for spreading other inflammatory content. We present the observed sentiment polarization using sentiment analysis for each cluster and derive the concern index for each cluster, which shows a measurable difference between the presidential candidates that seems to have been reflected in the election results.
AB - Social media, such as Twitter, have been exploited by trolls to manipulate political discourse and spread disinformation during the 2016 US Presidential Election. Trolls are users of social media accounts created with intentions to influence the public opinion by posting or reposting messages containing misleading or inflammatory information with malicious intentions. There has been previous research that focused on troll detection using Machine Learning approaches, and troll understanding using visualizations, such as word clouds. In this paper, we focus on the content analysis of troll tweets to identify the major entities mentioned and the relationships among these entities, to understand the events and statements mentioned in Russian Troll tweets coming from the Internet Research Agency (IRA), a troll factory allegedly financed by the Russian government. We applied several NLP techniques to develop Knowledge Graphs to understand the relationships of entities, often mentioned by dispersed trolls, and thus hard to uncover. This integrated KG helped to understand the substance of Russian Trolls' influence in the election. We identified three clusters of troll tweet content: one consisted of information supporting Donald Trump, the second for exposing and attacking Hillary Clinton and her family, and the third for spreading other inflammatory content. We present the observed sentiment polarization using sentiment analysis for each cluster and derive the concern index for each cluster, which shows a measurable difference between the presidential candidates that seems to have been reflected in the election results.
KW - Entity extraction
KW - Relationship analysis of troll tweets
KW - Sentiment analysis
KW - Triple extraction
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U2 - 10.5220/0010605403350342
DO - 10.5220/0010605403350342
M3 - Conference contribution
AN - SCOPUS:85111720759
T3 - Proceedings of the 10th International Conference on Data Science, Technology and Applications, DATA 2021
SP - 335
EP - 342
BT - Proceedings of the 10th International Conference on Data Science, Technology and Applications, DATA 2021
A2 - Quix, Christoph
A2 - Hammoudi, Slimane
A2 - van der Aalst, Wil
PB - SciTePress
T2 - 10th International Conference on Data Science, Technology and Applications, DATA 2021
Y2 - 6 July 2021 through 8 July 2021
ER -