TY - GEN
T1 - The Pod People
T2 - 29th International World Wide Web Conference, WWW 2020
AU - Weerasinghe, Janith
AU - Flanigan, Bailey
AU - Stein, Aviel
AU - McCoy, Damon
AU - Greenstadt, Rachel
N1 - Funding Information:
We thank the anonymous reviewers for their helpful comments and feedback, and Cynthia Gill and Jaime Richards for their contributions in the early stages of this project. Our work was supported by the National Science Foundation under grants 1931005 and 1814816.
Publisher Copyright:
© 2020 ACM.
PY - 2020/4/20
Y1 - 2020/4/20
N2 - Online Social Network (OSN) Users' demand to increase their account popularity has driven the creation of an underground ecosystem that provides services or techniques to help users manipulate content curation algorithms. One method of subversion that has recently emerged occurs when users form groups, called pods, to facilitate reciprocity abuse, where each member reciprocally interacts with content posted by other members of the group. We collect 1.8 million Instagram posts that were posted in pods hosted on Telegram. We first summarize the properties of these pods and how they are used, uncovering that they are easily discoverable by Google search and have a low barrier to entry. We then create two machine learning models for detecting Instagram posts that have gained interaction through two different kinds of pods, achieving 0.91 and 0.94 AUC, respectively. Finally, we find that pods are effective tools for increasing users' Instagram popularity, we estimate that pod utilization leads to a significantly increased level of likely organic comment interaction on users' subsequent posts.
AB - Online Social Network (OSN) Users' demand to increase their account popularity has driven the creation of an underground ecosystem that provides services or techniques to help users manipulate content curation algorithms. One method of subversion that has recently emerged occurs when users form groups, called pods, to facilitate reciprocity abuse, where each member reciprocally interacts with content posted by other members of the group. We collect 1.8 million Instagram posts that were posted in pods hosted on Telegram. We first summarize the properties of these pods and how they are used, uncovering that they are easily discoverable by Google search and have a low barrier to entry. We then create two machine learning models for detecting Instagram posts that have gained interaction through two different kinds of pods, achieving 0.91 and 0.94 AUC, respectively. Finally, we find that pods are effective tools for increasing users' Instagram popularity, we estimate that pod utilization leads to a significantly increased level of likely organic comment interaction on users' subsequent posts.
UR - http://www.scopus.com/inward/record.url?scp=85086587585&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85086587585&partnerID=8YFLogxK
U2 - 10.1145/3366423.3380256
DO - 10.1145/3366423.3380256
M3 - Conference contribution
AN - SCOPUS:85086587585
T3 - The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020
SP - 1874
EP - 1884
BT - The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020
PB - Association for Computing Machinery, Inc
Y2 - 20 April 2020 through 24 April 2020
ER -