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Journal articles on the topic 'Malicious posts on Twitter'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Sopinti Chaitanya Raj and B.Srinivas.S.P.Kumar. "DETECTING MALICIOUS TWITTER BOTS USING MACHINE LEARNING." international journal of engineering technology and management sciences 6, no. 6 (2022): 382–88. http://dx.doi.org/10.46647/ijetms.2022.v06i06.068.

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In today's world, Twitter is used often & has taken on significance in lives about many individuals, including businessmen, media, politicians, & others. One about most popular social networking sites, Twitter enables users towards share their opinions on a range about subjects, including politics, sports, financial market, entertainment, & more. It is one about fastest methods about information transfer. It significantly influences how individuals think. There are more people on Twitter who mask their identities for malicious reasons. Because it poses a risk towards other users, i

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Ibrahim, Yasser, Mohammed Abdel Razek, and Nasser El-Sherbeny. "Social cyber-criminal, towards automatic real time recognition of malicious posts on Twitter." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 1199. http://dx.doi.org/10.11591/ijeecs.v25.i2.pp1199-1207.

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Easy access to the internet throughout the world has fully reformed the usage of social communication such as Facebook, Twitter, Linked In which are becoming a part of our life. Accordingly, cybercrime has become a vital problem, especially in developing countries. The dissemination of information with no risk of being discovered and fetched leads to an increase in cyber-criminal. Meanwhile, the huge amount of data continuously produced from Twitter made the discovery process of cyber-criminals is a tough assignment. This research will contribute in determined on the build the comparable vecto

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Ali, Yasser Ibrahim Abdelmonem, Mohammed Abdel Razek, and Nasser El-Sherbeny. "Social cyber-criminal, towards automatic real time recognition of malicious posts on Twitter." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 1199–207. https://doi.org/10.11591/ijeecs.v25.i2.pp1199-1207.

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Easy access to the internet throughout the world has fully reformed the usage of social communication such as Facebook, Twitter, Linked In which are becoming a part of our life. Accordingly, cybercrime has become a vital problem, especially in developing countries. The dissemination of information with no risk of being discovered and fetched leads to an increase in cyber-criminal. Meanwhile, the huge amount of data continuously produced from Twitter made the discovery process of cyber-criminals is a tough assignment. This research will contribute in determined on the build the comparable vecto

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Dhanesh, Arya, Jyothika K, Malavika Jayaraj, Nevin Jose Antony, and Anu Treesa George. "Comprehensive Strategies for Identifying X(Twitter) Bots." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem43583.

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Twitter is a social network where users interact via text-based posts called tweets, using hashtags, mentions, shortened URLs, and retweets. The growing user base and open nature of Twitter have made it a target for automated programs, known as bots, which can be both beneficial and malicious. This research focuses on detecting and classifying Twitter accounts as human, bot, or cyborg. Given Twitter’s open nature, both helpful and harmful bots are prevalent, necessi- tating effective detection strategies. The study analyzes account behavior, content, and properties, introducing a classificatio

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Chang, Ying-Ying, Wei-Yao Wang, and Wen-Chih Peng. "SeGA: Preference-Aware Self-Contrastive Learning with Prompts for Anomalous User Detection on Twitter." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 1 (2024): 30–37. http://dx.doi.org/10.1609/aaai.v38i1.27752.

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In the dynamic and rapidly evolving world of social media, detecting anomalous users has become a crucial task to address malicious activities such as misinformation and cyberbullying. As the increasing number of anomalous users improves the ability to mimic normal users and evade detection, existing methods only focusing on bot detection are ineffective in terms of capturing subtle distinctions between users. To address these challenges, we proposed SeGA, preference-aware self-contrastive learning for anomalous user detection, which leverages heterogeneous entities and their relations in the

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Addawood, Aseel, Adam Badawy, Kristina Lerman, and Emilio Ferrara. "Linguistic Cues to Deception: Identifying Political Trolls on Social Media." Proceedings of the International AAAI Conference on Web and Social Media 13 (July 6, 2019): 15–25. http://dx.doi.org/10.1609/icwsm.v13i01.3205.

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The ease with which information can be shared on social media has opened it up to abuse and manipulation. One example of a manipulation campaign that has garnered much attention recently was the alleged Russian interference in the 2016 U.S. elections, with Russia accused of, among other things, using trolls and malicious accounts to spread misinformation and politically biased information. To take an in-depth look at this manipulation campaign, we collected a dataset of 13 million election-related posts shared on Twitter in 2016 by over a million distinct users. This dataset includes accounts

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Rodrigues, Anisha P., Roshan Fernandes, Aakash A, et al. "Real-Time Twitter Spam Detection and Sentiment Analysis using Machine Learning and Deep Learning Techniques." Computational Intelligence and Neuroscience 2022 (April 15, 2022): 1–14. http://dx.doi.org/10.1155/2022/5211949.

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In this modern world, we are accustomed to a constant stream of data. Major social media sites like Twitter, Facebook, or Quora face a huge dilemma as a lot of these sites fall victim to spam accounts. These accounts are made to trap unsuspecting genuine users by making them click on malicious links or keep posting redundant posts by using bots. This can greatly impact the experiences that users have on these sites. A lot of time and research has gone into effective ways to detect these forms of spam. Performing sentiment analysis on these posts can help us in solving this problem effectively.

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Herzallah, Wafa, Hossam Faris, and Omar Adwan. "Feature engineering for detecting spammers on Twitter: Modelling and analysis." Journal of Information Science 44, no. 2 (2017): 230–47. http://dx.doi.org/10.1177/0165551516684296.

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Twitter is a social networking website that has gained a lot of popularity around the world in the last decade. This popularity made Twitter a common target for spammers and malicious users to spread unwanted advertisements, viruses and phishing attacks. In this article, we review the latest research works to determine the most effective features that were investigated for spam detection in the literature. These features are collected to build a comprehensive data set that can be used to develop more robust and accurate spammer detection models. The new data set is tested using popular classif

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Leonardi, Simone, Giuseppe Rizzo, and Maurizio Morisio. "Automated Classification of Fake News Spreaders to Break the Misinformation Chain." Information 12, no. 6 (2021): 248. http://dx.doi.org/10.3390/info12060248.

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In social media, users are spreading misinformation easily and without fact checking. In principle, they do not have a malicious intent, but their sharing leads to a socially dangerous diffusion mechanism. The motivations behind this behavior have been linked to a wide variety of social and personal outcomes, but these users are not easily identified. The existing solutions show how the analysis of linguistic signals in social media posts combined with the exploration of network topologies are effective in this field. These applications have some limitations such as focusing solely on the fake

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Annisa, Fhena. "Ujaran Kebencian terhadap Artis K-Pop dalam Opening Ceremony FIFA World Cup 2022." Jurnal Komunikasi Global 12, no. 1 (2023): 71–97. http://dx.doi.org/10.24815/jkg.v12i1.31107.

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Kejuaraan Piala Dunia FIFA 2022 sempat menggemparkan jagat dunia maya usai keterlibatan artis K-pop sebagai salah satu pengisi acara pembukaan. Muncul sebagai bentuk implementasi tema yang diusung, yaitu multicultural, diversity, and peace, Jungkook BTS justru menjadi sasaran ujaran kebencian dari sejumlah penggemar sepak bola asal Indonesia di media sosial, khususnya Instagram dan Twitter. Tujuan penelitian ini yakni untuk mengetahui bentuk-bentuk ujaran kebencian para oknum penggemar sepak bola Indonesia terhadap Jungkook BTS di sejumlah unggahan media sosial. Penelitian dilakukan dengan pen

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Khaled, Kholoud, Abeer ElKorany, and Cherry A. Ezzat. "Enhancing prediction of user stance for social networks rumors." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (2023): 6609. http://dx.doi.org/10.11591/ijece.v13i6.pp6609-6619.

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The spread of social media has led to a massive change in the way information is dispersed. It provides organizations and individuals wider opportunities of collaboration. But it also causes an emergence of malicious users and attention seekers to spread rumors and fake news. Understanding user stances in rumor posts is very important to identify the veracity of the underlying content as news becomes viral in a few seconds which can lead to mass panic and confusion. In this paper, different machine learning techniques were utilized to enhance the user stance prediction through a conversation t

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Kholoud, Khaled, ElKorany Abeer, and A. Ezzat Cherry. "Enhancing prediction of user stance for social networks rumors." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (2023): 6609–19. https://doi.org/10.11591/ijece.v13i6.pp6609-6619.

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The spread of social media has led to a massive change in the way information is dispersed. It provides organizations and individuals wider opportunities of collaboration. But it also causes an emergence of malicious users and attention seekers to spread rumors and fake news. Understanding user stances in rumor posts is very important to identify the veracity of the underlying content as news becomes viral in a few seconds which can lead to mass panic and confusion. In this paper, different machine learning techniques were utilized to enhance the user stance prediction through a conversation t

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Zain, M. Syafrizal, Better Swengky, Bradika Almandin Wisesa, and Vivin Mahat Putri. "GAN-Based Approach for Identifying Fake Accounts on Twitter." Jurnal Pengembangan Sistem Informasi dan Informatika 6, no. 1 (2025): 42–54. https://doi.org/10.47747/jpsii.v6i1.2671.

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The multiple security threats on the network make the need for robust security measures a major concern. The increasing presence of fake accounts and malicious actors on online platforms poses significant challenges, requiring sophisticated detection techniques to maintain network integrity. To address these issues, we propose a novel method for detecting fake accounts by leveraging Generative Adversarial Networks (GANs). By analyzing data extracted from platform APIs, our approach leverages the unique characteristics of GANs to improve the accuracy and efficiency of the detection process. In

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Skumanich, Andy, and Han Kyul Kim. "Modes of Tracking Mal-Info in Social Media with AI/ML Tools to Help Mitigate Harmful GenAI for Improved Societal Well Being." Proceedings of the AAAI Symposium Series 3, no. 1 (2024): 412–17. http://dx.doi.org/10.1609/aaaiss.v3i1.31247.

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A rapidly developing threat to societal well-being is from misinformation widely spread on social media. Even more concerning is ”mal-info” (malicious) which is amplified on certain social networks. Now there is an additional dimension to that threat, which is the use of Generative AI to deliberately augment the mis-info and mal-info. This paper highlights some of the ”fringe” social media channels which have a high level of mal-info as characterized by our AI/ML algorithms. We discuss various channels and focus on one in particular, ”GAB”, as representative of the potential negative impacts.

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Ullmann, Stefanie, and Marcus Tomalin. "Quarantining online hate speech: technical and ethical perspectives." Ethics and Information Technology 22, no. 1 (2019): 69–80. http://dx.doi.org/10.1007/s10676-019-09516-z.

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Abstract In this paper we explore quarantining as a more ethical method for delimiting the spread of Hate Speech via online social media platforms. Currently, companies like Facebook, Twitter, and Google generally respond reactively to such material: offensive messages that have already been posted are reviewed by human moderators if complaints from users are received. The offensive posts are only subsequently removed if the complaints are upheld; therefore, they still cause the recipients psychological harm. In addition, this approach has frequently been criticised for delimiting freedom of e

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Al-Tarawneh, Ahmed, and Ja’afer Al-Saraireh. "Efficient detection of hacker community based on twitter data using complex networks and machine learning algorithm." Journal of Intelligent & Fuzzy Systems 40, no. 6 (2021): 12321–37. http://dx.doi.org/10.3233/jifs-210458.

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Twitter is one of the most popular platforms used to share and post ideas. Hackers and anonymous attackers use these platforms maliciously, and their behavior can be used to predict the risk of future attacks, by gathering and classifying hackers’ tweets using machine-learning techniques. Previous approaches for detecting infected tweets are based on human efforts or text analysis, thus they are limited to capturing the hidden text between tweet lines. The main aim of this research paper is to enhance the efficiency of hacker detection for the Twitter platform using the complex networks techni

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Hickey, Daniel, Daniel M. T. Fessler, Kristina Lerman, and Keith Burghardt. "X under Musk’s leadership: Substantial hate and no reduction in inauthentic activity." PLOS ONE 20, no. 2 (2025): e0313293. https://doi.org/10.1371/journal.pone.0313293.

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Numerous studies have reported an increase in hate speech on X (formerly Twitter) in the months immediately following Elon Musk’s acquisition of the platform on October 27th, 2022; relatedly, despite Musk’s pledge to “defeat the spam bots,” a recent study reported no substantial change in the concentration of inauthentic accounts. However, it is not known whether any of these trends endured. We address this by examining material posted on X from the beginning of 2022 through June 2023, the period that includes Musk’s full tenure as CEO. We find that the increase in hate speech just before Musk

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Osei-Mensah, Christiana Hammond Naa Korkor Leeyoo Watson-Nortey Belinda. "‘Why aren’t you married? The same reason you are not dead, it’s not my time’: Social Media Use of Hate Speech on Female Celebrities in Ghana." RESEARCH JOURNAL OF MASS COMMUNICATION AND INFORMATION TECHNOLOGY 8, no. 1 (2023): 109–31. http://dx.doi.org/10.56201/rjmcit.v8.no1.2022.pg109.131.

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This study is aimed at analysing the rhetoric of hate speech and the degree of pervasiveness in discriminating against the female celebrity?s identity on social media which is often centred on the question: „why aren?t you married or why haven?t you still given birth?? The paper sought to examine the nature of social media posts of five female celebrities who had experienced the phenomenon of flaming through hate speech and explore the adoptive strategies of these female celebrities to ward off perpetrators of flames. Data was cyber-ethnographically gathered from the Twitter and Facebook pages

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Ong, Yew Chuan, Stefania Paladini, Belal Alifan, Aceng Sambas, Sharifah Sumayyah Engku Alwi, and Nur Syakirah Mohd Sedek. "SohoNet: A Novel Social Honeynet Framework for Detecting Social Bots in Online Social Networks." Journal of Advanced Research Design 122, no. 1 (2024): 234–48. https://doi.org/10.37934/ard.122.1.234248.

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Online social networks (OSNs) are increasingly threatened by social bots – software-controlled accounts that mimic human users for various purposes. In this paper, we propose SohoNet, a novel social honeynet designed to identify, monitor, and detect these malicious entities. This innovative approach improves upon existing research by integrating multiple honeypots with a semi-automatic label engine, thereby significantly enhancing the accuracy of social bot detection. We deployed SohoNet on Platform X (formerly known as Twitter) to analyze activities during the 2022 Malaysian general election

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Amr, Amr, Entesar H. Ibraheem Eliwa, and Ahmed Omar. "Improving Arabic Spam classification in social media using hyperparameters tuning and Particle Swarm Optimization." Fusion: Practice and Applications 16, no. 1 (2024): 08–22. http://dx.doi.org/10.54216/fpa.160101.

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Online social networks continue to evolve, serving a variety of purposes, such as sharing educational content, chatting, making friends and followers, sharing news, and playing online games. However, the widespread flow of unwanted messages poses significant problems, including reducing online user interaction time, extremist views, reducing the quality of information, especially in the educational field. The use of coordinated automated accounts or robots on social networking sites is a common tactic for spreading unwanted messages, rumors, fake news, and false testimonies for mass communicat

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Kang, Seok, KyuJin Shim, and Jiyoun Kim. "Social Media Posts on Samsung Galaxy Note 7 Explosion: A Comparative Analysis of Crisis Framing and Sentiments in Three Nations." Journal of International Crisis and Risk Communication Research 2, no. 2 (2019): 259–90. http://dx.doi.org/10.30658/jicrcr.2.2.5.

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This study explores the Samsung Galaxy Note 7 explosion crisis by analyzing posts on Twitter in three nations: the United States, Australia, and South Korea. Using the perspectives of generic frames, issue-specific frames, cross-national frames, and user sentiment on Twitter, this study analyzes 600 posts (200 from each nation). Results reveal that Twitter posts frequently framed the crisis using attribution, morality, and conflict frames. Posts about the explosion were more professional frame oriented than national frame oriented. Negative sentiment was dominant in Twitter posts about the exp

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Guidry, Jeanine P. D., Ashlee N. Sawyer, Candace W. Burton, and Kellie E. Carlyle. "#NotOkay: Stories About Abuse on Instagram and Twitter." Partner Abuse 11, no. 2 (2020): 117–39. http://dx.doi.org/10.1891/pa-d-18-00037.

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Social media platforms like Twitter and Instagram have recently been used as venues for discussing abusive relationships and sexual violence. This study examines the ways in which social media users and how Twitter and Instagram users engaged with these posts by analyzing 500 Twitter and 500 Instagram posts focused on #NotOkay using a quantitative content analysis. There were marked differences between the posts on the two platforms as well as between engagement with these posts: Instagram overall elicited a higher median level of engagement than Twitter as well as more frequent expressions of

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O'Dea, Bridianne, Mark E. Larsen, Philip J. Batterham, Alison L. Calear, and Helen Christensen. "A Linguistic Analysis of Suicide-Related Twitter Posts." Crisis 38, no. 5 (2017): 319–29. http://dx.doi.org/10.1027/0227-5910/a000443.

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Abstract. Background: Suicide is a leading cause of death worldwide. Identifying those at risk and delivering timely interventions is challenging. Social media site Twitter is used to express suicidality. Automated linguistic analysis of suicide-related posts may help to differentiate those who require support or intervention from those who do not. Aims: This study aims to characterize the linguistic profiles of suicide-related Twitter posts. Method: Using a dataset of suicide-related Twitter posts previously coded for suicide risk by experts, Linguistic Inquiry and Word Count (LIWC) and regre

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Sneha, B. "Detecting Malicious Twitter Bots using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 1457–63. http://dx.doi.org/10.22214/ijraset.2024.59085.

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Abstract: Twitter play a significant role in our daily lives, offering a wide range of opportunities to their users. However, Twitter and online social networks (OSNs) in general are increasingly being utilized by automated accounts, commonly known as bots, as they continue to gain immense popularity across various user demographics. Malicious twitter Bots detection is required to detect real users from fraudulent users because it leads to spreading of spam messages and engage in fraudulent activities. To overcome this, we are going to differentiate bots from legitimate users using feature ext

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Alipour, Sanaz Adel, Rita Orji, and Nur Zincir-Heywood. "Behaviour and Bot Analysis on Online Social Networks." International Journal of Technology and Human Interaction 19, no. 1 (2023): 1–19. http://dx.doi.org/10.4018/ijthi.327789.

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The internet is home to a multitude of social networks that provide users with a sense of community and connection across the world. Among these, Twitter and Reddit are two of the most popular. While Twitter users follow and interact with other users (tweets), Reddit users follow and interact with communities known as subreddits. In addition to mainstream social networks, alternative platforms such as Parler exist for users who prefer less moderated online environments. However, there are also malicious users, such as bots and trolls, who exploit social networks for malicious purposes. Therefo

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N. Ezhil Arasi, Dr. G Manikandan, Ms. S. Hemalatha, and Ms. Vilma Veronica. "Malicious Social Bot Using Twitter Network Analysis in Django." International Journal of Scientific Research in Science and Technology 11, no. 2 (2024): 114–13. http://dx.doi.org/10.32628/ijsrst52411222.

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Malicious social bots generate fake tweets and automate their social relationships either by pretending to be a followers or by creating multiple fake accounts with malicious activities. Moreover, malicious social bots post shortened malicious URLs in the tweets to redirect the requests of online social networking participants to some malicious servers. Hence, distinguishing malicious social bots from legitimate users is one of the most important tasks in the Twitter network. To detect malicious social bots, extracting URL-based features (such as URL redirection, frequency of shared URLs, and

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Park, Yongsook. "A study on countermeasures related to malicious comments on the Internet." Korean Constitutional Law Association 28, no. 4 (2022): 367–99. http://dx.doi.org/10.35901/kjcl.2022.28.4.367.

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Comments on the Internet are the result of responding behavior of recipients(readers) to posts in the Internet space and are written in the form of messages, whereas malicious comments are intentionally posted content that criticizes or ridicules the publisher or a specific person regardless of the content. can Therefore, malicious comments include abusive language or slander against others, invasion of privacy, promotion of violence or speculative behavior, obscene language, and spamming posts. However, the problem related to malicious comments has occurred since the use of the Internet becam

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Cornet, V., and M. Hong. "COMPUTATIONAL EXPLORATION OF GERONTOLOGY-RELATED TOPICS SHARED ON SOCIAL MEDIA PLATFORM TWITTER." Innovation in Aging 2, suppl_1 (2018): 911. http://dx.doi.org/10.1093/geroni/igy031.3391.

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Abstract Twitter, a popular Internet social media platform, has become a significant medium for sharing information and ideas about various topics, including aging and older adults. While studies have been conducted to examine stigma or perception about specific diseases such as Alzheimer’s disease and seizure on Twitter, there has been little effort to identify general themes of Twitter posts related to aging and older adults. This exploratory study attempts to answer this need by conducting a topic analysis of posts shared on Twitter posts about aging and older adults in English. We collecte

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Yu, Jonathan S., James B. Carr, Jacob Thomas, et al. "Trends in Patient, Physician, and Public Perception of Ulnar Collateral Ligament Reconstruction Using Social Media Analytics." Orthopaedic Journal of Sports Medicine 9, no. 3 (2021): 232596712199005. http://dx.doi.org/10.1177/2325967121990052.

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Background: Social media posts regarding ulnar collateral ligament (UCL) injuries and reconstruction surgeries have increased in recent years. Purpose: To analyze posts shared on Instagram and Twitter referencing UCL injuries and reconstruction surgeries to evaluate public perception and any trends in perception over the past 3 years. Study Design: Cross-sectional study. Methods: A search of a 3-year period (August 2016 and August 2019) of public Instagram and Twitter posts was performed. We searched for >22 hashtags and search terms, including #TommyJohn, #TommyJohnSurgery, and #tornUCL. A

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Lee, Kent, Marilyn Abbott, Shiran Wang, and Jacob Lang. "The Uses of X/Twitter by Members of the TESOL Community." Canadian Journal of Learning and Technology 49, no. 3 (2024): 1–21. http://dx.doi.org/10.21432/cjlt28495.

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A lack of dialogue and collaboration between researchers and practitioners has been recognized in the field of second language education. Social media platforms such as X/Twitter have potential for connecting professionals in the teaching of English to speakers of other languages (TESOL) community and supporting professional learning and research; however, studies of TESOL professionals’ uses of X/Twitter have only examined posts/tweets from a limited number of communities marked by hashtags/ keywords. This study identifies 23 hashtags relevant to TESOL instruction for adults in the Canadian c

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Haeberle, Heather S., Nicholas I. Bartschat, Sergio M. Navarro, et al. "Hip Arthroscopy: A Social Media Analysis of Patient Perception." Orthopaedic Journal of Sports Medicine 7, no. 6 (2019): 232596711985418. http://dx.doi.org/10.1177/2325967119854188.

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Background: The rate of hip arthroscopy has increased significantly in recent years, although understanding of patient perception remains limited. Purpose: To analyze posts shared on Instagram and Twitter referencing hip arthroscopy to evaluate perspective, tone, timing, content, visibility, and location. Study Design: Cross-sectional study; Level of evidence, 3. Methods: A search of public posts on Instagram and Twitter was performed over a 1-year period, selected through use of the following hashtags: #hiparthroscopy, #hipscope, and #labralrepair. A total of 1850 Instagram posts and 163 Twit

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Araujo, Priscila Batista de, Laysa Fernanda Silva Pedrollo, and Kelly Graziani Giacchero Vedana. "Mental health and the Covid-19 pandemic: main topics published in Portuguese and Italian on Twitter." Saúde em Redes 10, no. 3 (2024): 4383. http://dx.doi.org/10.18310/2446-4813.2024v10n3.4383.

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Aim: Analyze published topics about mental health and the Covid-19 pandemic in public posts in Portuguese and Italian on Twitter. Methodology: Study of qualitative, cross-sectional approach, developed from the analysis of public Twitter posts in Portuguese and Italian language about mental health and the Covid-19 pandemic. The data were extracted, transcribed and the content were analyzed from Thematic Analysis. Results: A total of 430 Twitter posts were analyzed and five categories were created: Diverse criticisms about behavior and public policies; Restrictions and privations; The (in)attent

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M Nithin, Meer Eshak Ahammad, Nichenametla Shashank, Shaik Hyder Ali, and K.Mudduswamy. "Detection of Malicious Social Bots Using Learning Automata with URL Features in Twitter Network." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 3 (2025): 261–66. https://doi.org/10.32628/cseit2511317.

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With the rapid growth of social media platforms like Twitter, malicious social bots have become a significant threat, capable of manipulating public opinion, spreading misinformation, and launching cyber-attacks. These bots often mimic human behavior, making their detection a challenging task. This project proposes a novel approach to detect malicious social bots on Twitter by leveraging Learning Automata in combination with URL-based features extracted from user-generated content. The methodology involves analyzing embedded URLs in tweets—such as domain reputation, frequency, and redirection

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Heiss, Raffael, Christian von Sikorski, and Jörg Matthes. "Populist Twitter Posts in News Stories." Journalism Practice 13, no. 6 (2019): 742–58. http://dx.doi.org/10.1080/17512786.2018.1564883.

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Allem, Jon-Patrick, Likhit Dharmapuri, Jennifer B. Unger, and Tess Boley Cruz. "Characterizing JUUL-related posts on Twitter." Drug and Alcohol Dependence 190 (September 2018): 1–5. http://dx.doi.org/10.1016/j.drugalcdep.2018.05.018.

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Bokaei, Mohammad Hadi, Minoo Nassajian, Mojgan Farhoodi, and Mona Davoudi Shamsi. "Claim Detection in Persian Twitter Posts." International Journal of Information and Communication Technology Research 16, no. 3 (2024): 25–34. https://doi.org/10.61186/itrc.16.3.25.

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Zhu, Jane M., Abeed Sarker, Sarah Gollust, Raina Merchant, and David Grande. "Characteristics of Twitter Use by State Medicaid Programs in the United States: Machine Learning Approach." Journal of Medical Internet Research 22, no. 8 (2020): e18401. http://dx.doi.org/10.2196/18401.

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Background Twitter is a potentially valuable tool for public health officials and state Medicaid programs in the United States, which provide public health insurance to 72 million Americans. Objective We aim to characterize how Medicaid agencies and managed care organization (MCO) health plans are using Twitter to communicate with the public. Methods Using Twitter’s public application programming interface, we collected 158,714 public posts (“tweets”) from active Twitter profiles of state Medicaid agencies and MCOs, spanning March 2014 through June 2019. Manual content analyses identified 5 br

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Lakshmi, Dr K. N. S., and B. Madhu Sowmya. "FINDING RELATED FORUM POSTS THROUGH TWITTER DATA USING MATCHING ALGORITHM." International Journal of Engineering Applied Sciences and Technology 7, no. 11 (2023): 131–37. http://dx.doi.org/10.33564/ijeast.2023.v07i11.022.

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Forum posts has the specific problem of finding related posts to a post at hand. By considering across the related documents the contents of posts are generally consider are whole. Here similarity process are done between two posts with respective segments and should be of same intention. All posts are generally fragmented in the form of group to attain the goal bunches. Now similarities are generally cross view the forums with sections and that will be of same intention.

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De Souza, Izabella M., and Sharmila Pixy Ferris. "Social Media Marketing in Luxury Retail." International Journal of Online Marketing 5, no. 2 (2015): 18–36. http://dx.doi.org/10.4018/ijom.2015040102.

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This study examines the potentials of social media marketing for luxury retailers. Social media marketing tactics of three luxury retail brands (Barneys New York, Net-a-Porter.com, and Saks Fifth Avenue) were examined across three major social media sites (Facebook, Twitter and Instagram). All posts by the three retailers' posts on Facebook, Twitter and Instagram were recorded on over the course of a week. A content analysis revealed that different social media networks may be effectively used in different ways by luxury retailers. Facebook allows for the greatest connection with consumers (al

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Su, Leona Yi-Fan, Meaghan McKasy, Michael A. Cacciatore, Sara K. Yeo, Alexandria R. DeGrauw, and Jennifer Shiyue Zhang. "Generating Science Buzz: An Examination of Multidimensional Engagement With Humorous Scientific Messages on Twitter and Instagram." Science Communication 44, no. 1 (2021): 30–59. http://dx.doi.org/10.1177/10755470211063902.

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This study investigates the types of humor embedded in funny scientific posts on social media and their effects on engagement. We mapped the landscape of such posts on Twitter and Instagram through content analysis of their message attributes. Regression analyses were then conducted to examine how different humor types, communicative functions, and visual attributes were associated with liking, retweeting, and commenting. On Twitter, wordplay and satire were found to be positively related to posts’ engagement levels, while anthropomorphic humor was negatively associated with the presence of co

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Rachel Henzell, M., A. Margaret Knight, K. C. Morgaine, J. S. Antoun, and M. Farella. "A qualitative analysis of orthodontic-related posts on Twitter." Angle Orthodontist 84, no. 2 (2013): 203–7. http://dx.doi.org/10.2319/051013-355.1.

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ABSTRACT Objective: To investigate the content of orthodontic-related Twitter posts in order to improve our understanding of orthodontic patients' perceptions and treatment experiences. Materials and Methods: Some 131 consecutive posts were collected from Twitter over a 5-day period. Twitter's advanced-search function was used to limit the sampling frame to New Zealand. The posts were qualitatively analyzed for themes using discourse analysis. Results: Four main themes were identified: (1) excitement about getting braces off, (2) problems with braces, (3) positive comments about braces, and (4

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Popov, V. A., and A. A. Chepovskiy. "Twitter Data Import Models." Vestnik NSU. Series: Information Technologies 19, no. 2 (2021): 76–91. http://dx.doi.org/10.25205/1818-7900-2021-19-2-76-91.

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In this paper, the authors describe an algorithm for importing data from the social network Twitter and building weighted social graphs. To import data, the given posts are taken as a basis, users who have had any of the recorded interactions with them are downloaded. Further, the algorithm focuses on the given configuration and uses it to calculate the weights on the edges of the resulting graph. The configuration takes into account the type of user interaction with each other. The authors introduce the concept of (F, L, C, R)-model of information interaction.The authors describe the develope

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Kim, Meen Chul, Kyu Seung Shim, Nam Gi Han, Ye Eun Kim, and Min Song. "Automatic Classification of Malicious Usage on Twitter." Journal of the Korean Society for Library and Information Science 47, no. 1 (2013): 269–86. http://dx.doi.org/10.4275/kslis.2013.47.1.269.

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Oz, Mustafa, Pei Zheng, and Gina Masullo Chen. "Twitter versus Facebook: Comparing incivility, impoliteness, and deliberative attributes." New Media & Society 20, no. 9 (2017): 3400–3419. http://dx.doi.org/10.1177/1461444817749516.

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Using two quantitative methods, this study sought to understand whether user-generated posts would vary in frequency of incivility, impoliteness, and deliberative attributes on Twitter versus Facebook. A quantitative content analysis ( N = 1458) revealed that posts responding to the White House’s tweets were significantly more uncivil and impolite and less deliberative than responses to White House Facebook posts. Also, comments on posts that concerned sensitive topics (such as same-sex marriage) were more uncivil, impolite, and deliberative than comments regarding less sensitive topics (such

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Lee, Euijong, Young-Gab Kim, Young-Duk Seo, Kwangsoo Seol, and Doo-Kwon Baik. "An Evaluation Method for Content Analysis Based on Twitter Content Influence." International Journal of Software Engineering and Knowledge Engineering 27, no. 05 (2017): 841–67. http://dx.doi.org/10.1142/s0218194017500310.

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Twitter is a microblogging website, which has different characteristics from any other social networking service (SNS) in that it has one-directional relationships between users with short posts of less than 140 characters. These characteristics make Twitter not only a social network but also a news media. In addition, Twitter posts have been used and analyzed in various fields such as marketing, prediction of presidential elections, and requirement analysis. With an increase in Twitter usage, we need a more effective method to analyze Twitter content. In this paper, we propose a method for co

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Berestova, Anastasiia, Da-Yeon Kim, and Sang-Yong Kim. "Consumers’ Active Reaction to Brands Taking Stands on Public Issues on Twitter." Sustainability 14, no. 1 (2022): 567. http://dx.doi.org/10.3390/su14010567.

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With the growing interest in sustainability, brands increasingly use social media not simply to advertise their products but also to share their positions on ongoing public issues. This study investigates the effect of public-issue posts on consumers’ active reactions and provides comparisons between two samples—public-issue posts and all Twitter posts. After collecting data from Twitter based on tweets from official sportswear brand pages, we find that public-issue posts have a positive influence on consumers’ active reaction (i.e., number of retweets, number of quotes, number of replies, and

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Haigh, Michel M., and Pamela Jo Brubaker. "Social Media and Televangelists: Examining Facebook and Twitter Content." Journal of Religion, Media and Digital Culture 7, no. 1 (2018): 29–49. http://dx.doi.org/10.1163/25888099-00701003.

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A content analysis was conducted of televangelists’ Facebook and Twitter pages. Tweets and Facebook posts were coded for a period of two weeks. The posts/tweets were coded for a variety of categories including: dialogic features, content of the post, and theme. Televangelists tweet more often than they post to Facebook. Findings indicate most televangelists included links to their websites, an email address, links to other forms of social media, phone numbers, and additional content about their organizations. Only a third of the posts/tweets included a reference to the Lord/Jesus/Holy Spirit,

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Dormanesh, Allison, Anuja Majmundar, and Jon-Patrick Allem. "Follow-Up Investigation on the Promotional Practices of Electric Scooter Companies: Content Analysis of Posts on Instagram and Twitter." JMIR Public Health and Surveillance 6, no. 1 (2020): e16833. http://dx.doi.org/10.2196/16833.

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Background Electric scooters (e-scooters) have become a popular mode of transportation in both the United States and Europe. In the wake of this popularity, e-scooters have changed the commuting experience in many metropolitan areas. Although e-scooters offer an efficient and economical way to travel short distances in traffic-congested areas, recent studies have raised concerns over their safety. Bird and Tier Mobility are 2 popular e-scooter companies in the United States and Europe, respectively. Both companies maintain active social media accounts with hundreds of posts and tens of thousan

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Lobanov, Nikita. "From London Bridge to the Finsbury Park Mosque." European Journal of Humour Research 12, no. 1 (2024): 157–70. http://dx.doi.org/10.7592/ejhr.2024.12.1.834.

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The former Twitter (now called ‘X’) is a social media platform characterized by an intense exchange of posts by users on political themes. These posts can indicate the evolution of a social phenomenon around a pivotal event. The overarching theme of this paper is to observe and examine the posts of Twitter users following the London Bridge terror attack on June 3rd, 2017, and following another attack against the Finsbury Park Mosque on June 19th of the same month. By means of a content analysis of a robust database of posts collected from the Twitter platform I was able to observe the reaction

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Temel, Mustafa Hüseyin, and Fatih Bağcıer. "From hashtags to healing: Social media insights on kinesio taping in Türkiye." Journal of Surgery and Medicine 9, no. 6 (2025): 85–88. https://doi.org/10.28982/josam.8029.

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Background/Aim: Social media provides valuable insights into healthcare practices, including kinesio taping (KT), a technique used for pain relief and stability. Despite its popularity, there is limited research on KT content on social media. This study aims to analyze social media posts related to KT in Türkiye. Methods: The study was conducted by analyzing posts on social media platforms including Twitter, Instagram, Facebook, and YouTube. The hashtags ‘#kinezyobant’, ‘#kinezyobantlama’, and ‘#kinezyoteyp’, as well as the keywords ‘kinezyobant’, ‘kinezyobantlama’, and ‘kinezyoteyp’ were used

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