Authors:
Samir de O. Ramos
;
Ronaldo R. Goldschmidt
and
Alex de V. Garcia
Affiliation:
Section of Computer Engineering (SE/9), Military Institute of Engineering (IME), Rio de Janeiro, Brazil
Keyword(s):
Social Bot Detection, Machine Learning, Natural Language Processing, Sentiment Analysis.
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.
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