Effective Spam Bot Detection Using Glow Worm-Based Generalized Regression Neural Network

Praveena, M. D. Anto; Smys, S.

doi:10.1007/978-981-16-1866-6_34

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…Third, the in-built feature of Social Bearing that was used to remove bot-related content might not be the most optimal approach for removal of all bot-related tweets as certain advanced bot accounts that exactly mimic real user Twitter accounts in terms of Twitter user names and share/post content on Twitter after randomized time intervals (as opposed to the usual bots which usually share/post content after a fixed time interval) might be difficult to identify using this approach. Emerging works in the field of Natural Language Processing such as Tweez-Bot [151], Bot-DenseNet [152], Bot2Vec [153], Botter [154], and GlowWorm-based Generalized Regression [155] could be used for identifying such advanced bot accounts on Twitter. We could not implement any of these emerging works in our study due to the limited integration options provided by the Social Bearing research tool.…”

Section: Resultsmentioning

confidence: 99%

An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection

Thakur

Han

2022

COVID

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This paper presents the findings of an exploratory study on the continuously generating Big Data on Twitter related to the sharing of information, news, views, opinions, ideas, knowledge, feedback, and experiences about the COVID-19 pandemic, with a specific focus on the Omicron variant, which is the globally dominant variant of SARS-CoV-2 at this time. A total of 12,028 tweets about the Omicron variant were studied, and the specific characteristics of the tweets that were analyzed include sentiment, language, source, type, and embedded URLs. The findings of this study are manifold. First, from sentiment analysis, it was observed that 50.5% of tweets had a ‘neutral’ emotion. The other emotions—‘bad’, ‘good’, ‘terrible’, and ‘great’—were found in 15.6%, 14.0%, 12.5%, and 7.5% of the tweets, respectively. Second, the findings of language interpretation showed that 65.9% of the tweets were posted in English. It was followed by Spanish or Castillian, French, Italian, Japanese, and other languages, which were found in 10.5%, 5.1%, 3.3%, 2.5%, and <2% of the tweets, respectively. Third, the findings from source tracking showed that “Twitter for Android” was associated with 35.2% of tweets. It was followed by “Twitter Web App”, “Twitter for iPhone”, “Twitter for iPad”, “TweetDeck”, and all other sources that accounted for 29.2%, 25.8%, 3.8%, 1.6%, and <1% of the tweets, respectively. Fourth, studying the type of tweets revealed that retweets accounted for 60.8% of the tweets, it was followed by original tweets and replies that accounted for 19.8% and 19.4% of the tweets, respectively. Fifth, in terms of embedded URL analysis, the most common domain embedded in the tweets was found to be twitter.com, which was followed by biorxiv.org, nature.com, wapo.st, nzherald.co.nz, recvprofits.com, science.org, and other domains. Finally, to support research and development in this field, we have developed an open-access Twitter dataset that comprises Tweet IDs of more than 500,000 tweets about the Omicron variant, posted on Twitter since the first detected case of this variant on 24 November 2021.

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Section: Resultsmentioning

confidence: 99%

An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection

Thakur

Han

2022

COVID

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“…Gaurav et al [27] pinpointed account patterns types using machine learning mechanisms and provides intelligent clues that may be utilized as a robustness gauge for several systems. Several machine learning approaches for detecting malicious users have been suggested on Praveena [28] work based on glow worm optimization technique to in order to deal with a small set of features. The authors employed generalized regression neural network to train these features.…”

Section: Related Workmentioning

confidence: 99%

Hybrid-MELAu: A Hybrid Mixing Engineered Linguistic Features Based on Autoencoder for Social Bot Detection.

Hamida

Refouf

Drif

et al. 2022

IJCAI

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Social bots are defined as computer algorithms that generate massive amounts of obnoxious or meaningful information. Most bot detection methods leverage multitudinous characteristics, from network features, temporal dynamics features, activities features, and sentiment features. However, there has been fairly lower work exploring lexicon measurement and linguistic indicators to detect bots. The main purpose of this research is to recognize the social bots through their writing style. Thus, we carried out an exploratory study on the effectiveness of only a set of linguistic features (17 features) exploitable for bot detection, without the need to resort to other types of features. And we develop a novel framework in a hybrid fashion of Mixing Engineered Linguistic features based on Autoencoders (Hybrid-MELAu). The semisupervised Hybrid-MELAu framework is composed of two essential constituents: the features learner and the predictors. We establish the features learner innovated on two powerful structures: a) the first is a Deep dense Autoencoder fed by the Lexical and the Syntactic content (DALS) that represents the high order lexical and syntactic features in latent space, b) the second one is a Glove-BiLSTM autoencoder, which sculpts the semantic features; subsequently, we generate elite elements from the pre-trained encoder part from each latent space with transfer learning. We consider a sample of 1 Million from Cresci datasets to conduct our linguistic analysis comparison between the writing style of humans and bots. With this dataset, we observe that the bot's textual lexical diversity median is greater than the human one and the syntactic analysis based on speech-tagging shows a creative behavior in human writing style. Finally, we test the model's robustness on several public dataset (celebrity, pronbots-2019, and political bots). The proposed framework achieves a good accuracy of 92.22%. Overall, the results shown in this paper, and the related discussion, argue that it is possible to discern the differences between humans' and bots' writing styles based on an efficient linguistic deep framework.Povzetek: V prispevku je opisana metoda za detekcijo pogovornih asistentov (botov) na osnovi jezkovnega stila.

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“…Third, the in-built feature of Social Bearing that was used to remove potential bot-related content might not be the most optimal approach for the removal of all bot-related tweets, as certain advanced bot accounts that exactly mimic real user Twitter accounts in terms of Twitter usernames and share/post content on Twitter after randomized time intervals (as opposed to certain bots which share/post content after a fixed time interval) might be difficult to identify using this approach. Emerging works in the field of Natural Language Processing, such as TweezBot [151], Bot-DenseNet [152], Bot2Vec [153], Botter [154], and GlowWorm-based Generalized Regression [155], could be used for identifying such advanced bot accounts on Twitter. We could not implement any of these emerging works in our study due to the limited integration options provided by the Social Bearing research tool.…”

mentioning

confidence: 99%

An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection

Thakur¹,

Han²

2022

Preprint

View full text Add to dashboard Cite

This paper presents the findings of an exploratory study on the continuously generating Big Data on Twitter related to the sharing of information, news, views, opinions, ideas, knowledge, feedback, and experiences about the COVID-19 pandemic, with a specific focus on the Omicron variant, which is the globally dominant variant of SARS-CoV-2 at this time. A total of 12028 tweets about the Omicron variant were studied, and the specific characteristics of tweets that were analyzed include - sentiment, language, source, type, and embedded URLs. The findings of this study are manifold. First, from sentiment analysis, it was observed that 50.5% of tweets had the ‘neutral’ emotion. The other emotions - ‘bad’, ‘good’, ‘terrible’, and ‘great’ were found in 15.6%, 14.0%, 12.5%, and 7.5% of the tweets, respectively. Second, the findings of language interpretation showed that 65.9% of the tweets were posted in English. It was followed by Spanish or Castillian, French, Italian, Japanese, and other languages, which were found in 10.5%, 5.1%, 3.3%, 2.5%, and &lt;2% of the tweets, respectively. Third, the findings from source tracking showed that “Twitter for Android” was associated with 35.2% of tweets. It was followed by “Twitter Web App”, “Twitter for iPhone”, “Twitter for iPad”, “TweetDeck”, and all other sources that accounted for 29.2%, 25.8%, 3.8%, 1.6%, and &lt;1% of the tweets, respectively. Fourth, studying the type of tweets revealed that retweets accounted for 60.8% of the tweets, it was followed by original tweets and replies that accounted for 19.8% and 19.4% of the tweets, respectively. Finally, in terms of embedded URL analysis, the most common URLs embedded in the tweets were found to be twitter.com, which was followed by biorxiv.org, nature.com, wapo.st, nzherald.co.nz, recvprofits.com, science.org, and other URLs.

show abstract

Effective Spam Bot Detection Using Glow Worm-Based Generalized Regression Neural Network

Cited by 4 publications

References 27 publications

An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection

An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection

Hybrid-MELAu: A Hybrid Mixing Engineered Linguistic Features Based on Autoencoder for Social Bot Detection.

An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection

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