TY - GEN
T1 - Did State-Sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter
AU - Salamanos, Nikos
AU - Jensen, Michael J.
AU - Iordanou, Costas
AU - Sirivianos, Michael
N1 - Funding Information:
We are grateful to Twitter for providing access to the trolls’ ground truth dataset. We thank Nikolaos Laoutaris for his insightful comments about the Shapley Value. This project has received funding from the European Union’s Horizon 2020 Research and Innovation program under the Cybersecurity CONCORDIA project (Grant Agreement No. 830927) and under the Marie Skłodowska-Curie INCOGNITO project (Grant Agreement No. 824015).
Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - It is a widely accepted fact that state-sponsored Twitter accounts operated during the 2016 US presidential election, spreading millions of tweets with misinformation and inflammatory political content. Whether these social media campaigns of the so-called “troll” accounts were able to manipulate public opinion is still in question. Here, we quantify the influence of troll accounts on Twitter by analyzing 152.5 million tweets (by 9.9 million users) from that period. The data contain original tweets from 822 troll accounts identified as such by Twitter. We construct and analyze a very large interaction graph of 9.3 million nodes and 169.9 million edges using graph analysis techniques and a game-theoretic centrality measure. Then, we quantify the influence of all Twitter accounts on the overall information exchange as defined by the retweet cascades. We provide a global influence ranking of all Twitter accounts, and we find that one troll account appears in the top-100 and four in the top-1000. This, combined with other findings presented in this paper, constitute evidence that the driving force of virality and influence in the network came from regular users - users who have not been classified as trolls by Twitter. On the other hand, we find that, on average, troll accounts were tens of times more influential than regular users were. Moreover, 23% and 22% of regular accounts in the top-100 and top-1000, respectively, have now been suspended by Twitter. This raises questions about their authenticity and practices during the 2016 US presidential election.
AB - It is a widely accepted fact that state-sponsored Twitter accounts operated during the 2016 US presidential election, spreading millions of tweets with misinformation and inflammatory political content. Whether these social media campaigns of the so-called “troll” accounts were able to manipulate public opinion is still in question. Here, we quantify the influence of troll accounts on Twitter by analyzing 152.5 million tweets (by 9.9 million users) from that period. The data contain original tweets from 822 troll accounts identified as such by Twitter. We construct and analyze a very large interaction graph of 9.3 million nodes and 169.9 million edges using graph analysis techniques and a game-theoretic centrality measure. Then, we quantify the influence of all Twitter accounts on the overall information exchange as defined by the retweet cascades. We provide a global influence ranking of all Twitter accounts, and we find that one troll account appears in the top-100 and four in the top-1000. This, combined with other findings presented in this paper, constitute evidence that the driving force of virality and influence in the network came from regular users - users who have not been classified as trolls by Twitter. On the other hand, we find that, on average, troll accounts were tens of times more influential than regular users were. Moreover, 23% and 22% of regular accounts in the top-100 and top-1000, respectively, have now been suspended by Twitter. This raises questions about their authenticity and practices during the 2016 US presidential election.
KW - Disinformation
KW - Information Diffusion
KW - Political Trolls
KW - Twitter Trolls
UR - http://www.scopus.com/inward/record.url?scp=85172225013&partnerID=8YFLogxK
UR - https://nss-socialsec2023.cyber.kent.ac.uk/program.php
U2 - 10.1007/978-981-99-5177-2_4
DO - 10.1007/978-981-99-5177-2_4
M3 - Conference contribution
AN - SCOPUS:85172225013
SN - 9789819951765
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1
EP - 13
BT - Security and Privacy in Social Networks and Big Data - 9th International Symposium, SocialSec 2023, Proceedings
A2 - Arief, Budi
A2 - Monreale, Anna
A2 - Sirivianos, Michael
A2 - Li, Shujun
PB - Springer
T2 - Security and Privacy in Social Networks and Big Data - 9th International Symposium, SocialSec 2023, Proceedings
Y2 - 14 August 2023 through 16 August 2023
ER -