Abstract

The detection of state-sponsored trolls operating in influence campaigns on social media is a critical and unsolved challenge for the research community, which has significant implications beyond the online realm. To address this challenge, we propose a new AI-based solution that identifies troll accounts solely through behavioral cues associated with their sequences of sharing activity, encompassing both their actions and the feedback they receive from others. Our approach does not incorporate any textual content shared and consists of two steps: First, we leverage an LSTM-based classifier to determine whether account sequences belong to a state-sponsored troll or an organic, legitimate user. Second, we employ the classified sequences to calculate a metric named the “Troll Score”, quantifying the degree to which an account exhibits troll-like behavior. To assess the effectiveness of our method, we examine its …

Metadata

publication

EPJ Data Science 12 (1), 46, 2023

year

2023

publication date

2023/12/1

authors

Fatima Ezzeddine, Omran Ayoub, Silvia Giordano, Gianluca Nogara, Ihab Sbeity, Emilio Ferrara, Luca Luceri

link

https://epjds.epj.org/articles/epjdata/abs/2023/01/13688_2023_Article_423/13688_2023_Article_423.html

resource_link

https://link.springer.com/content/pdf/10.1140/epjds/s13688-023-00423-4.pdf

journal

EPJ Data Science

volume

12

issue

1

pages

46

publisher

Springer Berlin Heidelberg