@inproceedings{nasrin-etal-2019-many,
title = "How Many Users Are Enough? Exploring Semi-Supervision and Stylometric Features to Uncover a {R}ussian Troll Farm",
author = "Nasrin, Nayeema and
Raymond Choo, Kim-Kwang and
Ko, Myung and
Rios, Anthony",
editor = "Feldman, Anna and
Da San Martino, Giovanni and
Barr{\'o}n-Cede{\~n}o, Alberto and
Brew, Chris and
Leberknight, Chris and
Nakov, Preslav",
booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5003/",
doi = "10.18653/v1/D19-5003",
pages = "20--30",
abstract = "Social media has reportedly been (ab)used by Russian troll farms to promote political agendas. Specifically, state-affiliated actors disguise themselves as native citizens of the United States to promote discord and promote their political motives. Therefore, developing methods to automatically detect Russian trolls can ensure fair elections and possibly reduce political extremism by stopping trolls that produce discord. While data exists for some troll organizations (e.g., Internet Research Agency), it is challenging to collect ground-truth accounts for new troll farms in a timely fashion. In this paper, we study the impact the number of labeled troll accounts has on detection performance. We analyze the use of self-supervision with less than 100 troll accounts as training data. We improve classification performance by nearly 4{\%} F1. Furthermore, in combination with self-supervision, we also explore novel features for troll detection grounded in stylometry. Intuitively, we assume that the writing style is consistent across troll accounts because a single troll organization employee may control multiple user accounts. Overall, we improve on models based on words features by {\textasciitilde}9{\%} F1."
}
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<abstract>Social media has reportedly been (ab)used by Russian troll farms to promote political agendas. Specifically, state-affiliated actors disguise themselves as native citizens of the United States to promote discord and promote their political motives. Therefore, developing methods to automatically detect Russian trolls can ensure fair elections and possibly reduce political extremism by stopping trolls that produce discord. While data exists for some troll organizations (e.g., Internet Research Agency), it is challenging to collect ground-truth accounts for new troll farms in a timely fashion. In this paper, we study the impact the number of labeled troll accounts has on detection performance. We analyze the use of self-supervision with less than 100 troll accounts as training data. We improve classification performance by nearly 4% F1. Furthermore, in combination with self-supervision, we also explore novel features for troll detection grounded in stylometry. Intuitively, we assume that the writing style is consistent across troll accounts because a single troll organization employee may control multiple user accounts. Overall, we improve on models based on words features by ~9% F1.</abstract>
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%0 Conference Proceedings
%T How Many Users Are Enough? Exploring Semi-Supervision and Stylometric Features to Uncover a Russian Troll Farm
%A Nasrin, Nayeema
%A Raymond Choo, Kim-Kwang
%A Ko, Myung
%A Rios, Anthony
%Y Feldman, Anna
%Y Da San Martino, Giovanni
%Y Barrón-Cedeño, Alberto
%Y Brew, Chris
%Y Leberknight, Chris
%Y Nakov, Preslav
%S Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F nasrin-etal-2019-many
%X Social media has reportedly been (ab)used by Russian troll farms to promote political agendas. Specifically, state-affiliated actors disguise themselves as native citizens of the United States to promote discord and promote their political motives. Therefore, developing methods to automatically detect Russian trolls can ensure fair elections and possibly reduce political extremism by stopping trolls that produce discord. While data exists for some troll organizations (e.g., Internet Research Agency), it is challenging to collect ground-truth accounts for new troll farms in a timely fashion. In this paper, we study the impact the number of labeled troll accounts has on detection performance. We analyze the use of self-supervision with less than 100 troll accounts as training data. We improve classification performance by nearly 4% F1. Furthermore, in combination with self-supervision, we also explore novel features for troll detection grounded in stylometry. Intuitively, we assume that the writing style is consistent across troll accounts because a single troll organization employee may control multiple user accounts. Overall, we improve on models based on words features by ~9% F1.
%R 10.18653/v1/D19-5003
%U https://aclanthology.org/D19-5003/
%U https://doi.org/10.18653/v1/D19-5003
%P 20-30
Markdown (Informal)
[How Many Users Are Enough? Exploring Semi-Supervision and Stylometric Features to Uncover a Russian Troll Farm](https://aclanthology.org/D19-5003/) (Nasrin et al., NLP4IF 2019)
ACL