Bot detection in twitter landscape using unsupervised learning

Anwar, Ahmed; Yaqub, Ussama

doi:10.1145/3396956.3401801

Cited by 13 publications

(7 citation statements)

References 5 publications

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“…The vectorizer was used to tokenize the document and calculate the weights for each term. In another study [5], Twitter metadata was used to derive 13 unique characteristics. In the dataset provided, every single feature was a good representative for the variance present.…”

Section: Related Workmentioning

confidence: 99%

TweezBot: An AI-Driven Online Media Bot Identification Algorithm for Twitter Social Networks

2022

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In the ultra-connected age of information, online social media platforms have become an indispensable part of our daily routines. Recently, this online public space is getting largely occupied by suspicious and manipulative social media bots. Such automated deceptive bots often attempt to distort ground realities and manipulate global trends, thus creating astroturfing attacks on the social media online portals. Moreover, these bots often tend to participate in duplicitous activities, including promotion of hidden agendas and indulgence in biased propagation meant for personal gain or scams. Thus, online bots have eventually become one of the biggest menaces for social media platforms. Therefore, we have proposed an AI-driven social media bot identification framework, namely TweezBot, which can identify fraudulent Twitter bots. The proposed bot detection method analyzes Twitter-specific user profiles having essential profile-centric features and several activity-centric characteristics. We have constructed a set of filtering criteria and devised an exhaustive bag of words for performing language-based processing. In order to substantiate our research, we have performed a comparative study of our model with the existing benchmark classifiers, such as Support Vector Machine, Categorical Naïve Bayes, Bernoulli Naïve Bayes, Multilayer Perceptron, Decision Trees, Random Forest and other automation identifiers.

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Section: Related Workmentioning

confidence: 99%

TweezBot: An AI-Driven Online Media Bot Identification Algorithm for Twitter Social Networks

2022

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“…Over the years, researchers had analyzed various aspects of social media users. For example, a variety of algorithms were introduced to detect users such as bots, spam, fake accounts, influencers, and cyborgs [18], [22], [28], [29]. These algorithms examined behavior, content, action, and interaction-based features for user classification [30].…”

Section: Related Workmentioning

confidence: 99%

“…For instance, algorithms are introduced for the identification of trending topics and their classification into different categories [15], [16], [17]. In addition, Twitter content along with user behavior is examined to identify users such as bots [18], [19], [20], [21], spam users [22], [23], and fake accounts [24], [25]. Also, Twitter trending hashtags and topics are studied to understand traffic manipulation [4], political manipulation [26], astroturfing attacks [6], and role of bot accounts in manipulation [27].…”

Section: Introductionmentioning

confidence: 99%

Push-to-Trend: A Novel Framework to Detect Trend Promoters in Trending Hashtags

2022

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Twitter trends have enabled the speedy dissemination of information with the ability to affect public opinion. Unfortunately, fake trends are also generated by malicious users to mislead the public. In general, Twitter users are studied in depth to identify humans, bots, spam, and fake accounts. However, artificial intelligence algorithms are not developed for the identification of 'trend promoters' generating fake trends. In this paper, we propose Push-To-Trend -a novel framework to detect 'trend promoters' in trending hashtags. For this purpose, first, we develop a dataset of TREP-21 containing 3, 900 users labelled into two categories of 'trend promoters' and 'normal users'. In addition, we design four discerning features of number of total tweets, duplicate tweets, overlapping ngram, and peak-to-mean ratio for trend promoters classification. Moreover, we thoroughly examine the features used for spam and bot accounts classification to filter three efficacious features for trend promoters identification. Leveraging these seven features, Push-To-Trend achieves the accuracy of 0.97 for TREP-21. Furthermore, we leverage our framework to identify and analyze trend promoters from the Urdu tweets repository ''Anbar'' which consists of 106.9 million tweets and 1.69 million users. The analysis of 602 most frequent hashtags in Anbar reveals that 15.7% of trend promoters generate 68.1% of total tweets related to hashtags. To the best of our knowledge, this is the first attempt to design machine learning models for the automatic classification of trend promoters. As such, our framework is generic and adaptable for tweets posted in different natural languages as it utilizes languageindependent features. INDEX TERMSTwitter trends, trend promoters, social media user classification, Twitter analytics.

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“…Recently, social media analysis has been adopted to perform topic based sentiment analysis [18], [19], public opinionmining [20], emotion analysis [21], health surveillance [22], crime monitoring [23], [24], spam detection [25], [26], crisis management [27]- [29], and business marketing [4]. In addition, the social media users are examined for malicious user identification [30]- [33], location inference [34], and influencer identification [35].…”

Section: Related Workmentioning

confidence: 99%

“…Among the user-based features, the feature of geo-location turned out to be the most informative feature for the identification of bot users. Similarly, Anwar et al used an unsupervised learning approach for bot detection in Twitter [33]. The data from Canadian elections 2019 was used to perform k-means clustering with the feature-set containing the number of daily tweets, retweet percentage, and daily favourite count.…”

Section: B Bot Classificationmentioning

confidence: 99%

Towards Understanding Trends Manipulation in Pakistan Twitter

Kausar¹,

Tahir²,

Mehmood³

2021

Preprint

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The rapid adoption of online social media platforms has transformed the way of communication and interaction. On these platforms, discussions in the form of trending topics provide a glimpse of events happening around the world in real-time. Also, these trends are used for political campaigns, public awareness, and brand promotions. Consequently, these trends are sensitive to manipulation by malicious users who aim to mislead the mass audience. In this article, we identify and study the characteristics of users involved in the manipulation of Twitter trends in Pakistan. We propose 'Manipify' -a framework for automatic detection and analysis of malicious users for Twitter trends. Our framework consists of three distinct modules: i) user classifier, ii) hashtag classifier, and ii) trend analyzer. The user classifier introduces a novel approach to automatically detect manipulators using tweet content and user behaviour features. Also, the module classifies human and bot users. Next, the hashtag classifier categorizes trending hashtags into six categories assisting in examining manipulators behaviour across different categories. Finally, the trend analyzer module examines users, hashtags, and tweets for hashtag reach, linguistic features and user behaviour. Our user classifier module achieves 0.91 accuracy in classifying the manipulators. We further test Manipify on the dataset comprising of 665 trending hashtags with 5.4 million tweets and 1.9 million users. The analysis of trends reveals that the trending panel is mostly dominated by political hashtags. In addition, our results show a higher contribution of human accounts in trend manipulation as compared to bots. Furthermore, we present two case studies of hashtag-wars and anti-state propaganda to implicate the real-world application of our research.

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Bot detection in twitter landscape using unsupervised learning

Cited by 13 publications

References 5 publications

TweezBot: An AI-Driven Online Media Bot Identification Algorithm for Twitter Social Networks

TweezBot: An AI-Driven Online Media Bot Identification Algorithm for Twitter Social Networks

Push-to-Trend: A Novel Framework to Detect Trend Promoters in Trending Hashtags

Towards Understanding Trends Manipulation in Pakistan Twitter

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