TY - JOUR
T1 - A survey of extremism online content analysis and prediction techniques in twitter based on sentiment analysis
AU - Trabelsi, Zouheir
AU - Saidi, Firas
AU - Thangaraj, Eswari
AU - Veni, T.
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Nature Limited.
PY - 2022
Y1 - 2022
N2 - Nowadays, extremist organizations use social networks, such as Twitter, to flourish their dark activities. Usually, to polarize new members, these organizations attempt to share their radical propaganda by posting tweets. Practically, Sentiment Analysis (SA) techniques are widely used to classify the polarity of these extremist tweets, to derive appropriate conclusions for decision-making purposes, and to make valuable predictions about future violent and terrorist events. To study the influence of social networks-based malicious activities on human security and safety, this paper surveyed different Machine and Deep Learning based techniques used for Tweets SA, and discussed their strengths and weaknesses and recent trends in the field. Furthermore, the conducted survey work highlighted promising key areas and potential challenges that require further consideration to implement more effective methods to combat extremism in online social networks.
AB - Nowadays, extremist organizations use social networks, such as Twitter, to flourish their dark activities. Usually, to polarize new members, these organizations attempt to share their radical propaganda by posting tweets. Practically, Sentiment Analysis (SA) techniques are widely used to classify the polarity of these extremist tweets, to derive appropriate conclusions for decision-making purposes, and to make valuable predictions about future violent and terrorist events. To study the influence of social networks-based malicious activities on human security and safety, this paper surveyed different Machine and Deep Learning based techniques used for Tweets SA, and discussed their strengths and weaknesses and recent trends in the field. Furthermore, the conducted survey work highlighted promising key areas and potential challenges that require further consideration to implement more effective methods to combat extremism in online social networks.
KW - Machine learning (ML)
KW - Opinion mining (OM)
KW - Social networks extremism
KW - Twitter sentiment analysis (TSA)
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U2 - 10.1057/s41284-022-00335-4
DO - 10.1057/s41284-022-00335-4
M3 - Article
AN - SCOPUS:85128345499
SN - 0955-1662
JO - Security Journal
JF - Security Journal
ER -