Social Bots Detection: A Method based on a Sentiment Lexicon Learned from Messages

Samir Ramos, Ronaldo Goldschmidt, Alex Garcia

2022

Abstract

The use of bots on social networks for malicious purposes has grown significantly in recent years. Among the last generation techniques used in the automatic detection of social bots, are those that take into account the sentiment existing in the messages propagated on the network. This information is calculated based on sentiment lexicons with content manually annotated and, hence, susceptible to subjectivity. In addition, words are analyzed in isolation, without taking into account the context in which they are inserted, which may not be sufficient to express the sentiment existing in the sentence. With these limitations, this work raises the hypothesis that the automatic detection of social bots that considers the sentiment characteristics of the words of the messages can be improved if these characteristics were previously learned by machines from the data, instead of using manually annotated lexicons. To verify such hypothesis, this work proposes a method that detects bots based on Sentiment-Specific Word Embedding (SSWE), a lexicon of sentiment learned by a homonymous recurrent neural network, trained in a large volume of messages. Preliminary experiments carried out with data from Twitter have generated evidence that suggests the adequacy of the proposed method and confirms the raised hypothesis.

Paper Citation

in Harvard Style

Ramos S., Goldschmidt R. and Garcia A. (2022). Social Bots Detection: A Method based on a Sentiment Lexicon Learned from Messages. In Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-569-2, pages 273-280. DOI: 10.5220/0011115000003179

in Bibtex Style

@conference{iceis22,
author={Samir Ramos and Ronaldo Goldschmidt and Alex Garcia},
title={Social Bots Detection: A Method based on a Sentiment Lexicon Learned from Messages},
booktitle={Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2022},
pages={273-280},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011115000003179},
isbn={978-989-758-569-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Social Bots Detection: A Method based on a Sentiment Lexicon Learned from Messages
SN - 978-989-758-569-2
AU - Ramos S.
AU - Goldschmidt R.
AU - Garcia A.
PY - 2022
SP - 273
EP - 280
DO - 10.5220/0011115000003179