Relevant bibliographies by topics / Malicious posts on Twitter

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Malicious posts on Twitter'

Author: Grafiati
Published: 5 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Malicious posts on Twitter"

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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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Dissertations / Theses on the topic "Malicious posts on Twitter"

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Cushing, Cailin. "DETECTING NETFLIX SERVICE OUTAGES THROUGH ANALYSIS OF TWITTER POSTS." DigitalCommons@CalPoly, 2012. https://digitalcommons.calpoly.edu/theses/723.

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Every week there are over a billion new posts to Twitter services and many of those messages contain feedback to companies about their services. One company that has recognized this unused source of information is Netflix. That is why Netflix initiated the development of a system that will let them respond to the millions of Twitter and Netflix users that are acting as sensors and reporting all types of user visible outages. This system will enhance the feedback loop between Netflix and its customers by increasing the amount of customer feedback that is being received by Netflix and reducing t
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Nicklasson, Maja. "Posts with highest engagement! A study that identify posts on social media that generate most interactions by educational companies." Thesis, Malmö högskola, Fakulteten för teknik och samhälle (TS), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20209.

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Organisationer och företag använder idag frekvent sociala medier för att skapa deltagande hos sin målgrupp. I vårt digitala samhälle och stora mediebrus behövs strategier och medvetenhet kring vilka inlägg i sociala medier som lyckas generera högst deltagande. Denna uppsats identifierar utbildningsföretags inlägg på sociala medier som lett till högst deltagande. Syftet med uppsatsen är att finna eventuella mönster i dessa. Syftet är även att bidra med aktuell kunskap som utbildningsföretag, och företag i allmänhet, kan ta del av i sitt arbete med att skapa deltagande genom sociala medier. Upps
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Alghamdi, Bandar Abdulrahman. "Topic-based feature selection and a hybrid approach for detecting spammers on twitter." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/204112/1/Bandar%20Abdulrahman%20A_Alghamdi_Thesis.pdf.

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This thesis is an application of text mining techniques on Twitter to detect harmful users known as spam users. It examines users' posted content and characteristics to understand harmful activities and detect them. The thesis proposed methods to identify a set of new features that can accurately represent users' behavior, and also proposed a novel two-stage approach to detect spam users based on the features. The experiments conducted in the thesis work showed the effectiveness of the proposed features and the two-stage approach in detecting spam users. The thesis work made contributions to c
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Nelzén, Amanda. "Aristotle on social media? : Investigating non-profit organizations’ usage of persuasive language in their posts on Twitter and Facebook." Thesis, Linnéuniversitetet, Institutionen för språk (SPR), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-70001.

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This study investigates how non-profit organizations market their organizations and persuades their audience on two different social media platforms. The aim is to examine four non-profit organizations’ use of persuasive language in their Facebook and Twitter posts.  in their Facebook and Twitter posts on their social media pages. The aim is to understand if, how and what linguistic means was are used in the posts through Aristotle’s triangle of rhetoric. His theory consists of the three appeals: ethos, logos and pathos. These three appeals all holds a number of individual features.  with a pe
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Mohammed, Zuhura. "Crisis Communication and Management using SocialMedia: a Crisis Response to Ethiopian Airlines ET302 Crash." Thesis, Örebro universitet, Institutionen för humaniora, utbildnings- och samhällsvetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-85637.

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On the Sunday 10th of March 2019 Nairobi-bound Boeing 737MAX8 Flight ET302 of EthiopianAirlines plunge in to the ground and kills all 157 passengers onboard. Consequently, the accidenttook attention of international media and people around the world. This study therefore aims atinvestigating the communication strategies employed by EAL when managing ET302 planecrash and various issues associated with the crash using the two stages of crisis management. Toachieve the purpose, Nethnography and CDA methods were employed. To analyze thecommunication strategies SCCT was used. Accordingly, it was fo
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Venkatesh, Rasula. "Malicious Accounts Detection Based on Short URLs in Twitter." Thesis, 2015. http://ethesis.nitrkl.ac.in/7787/1/2015_btech_malicious_venkatesh.pdf.

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The popularity of Social Networks during the last several years has attracted attention of cybercriminals for the spreading of spam and malicious contents. In order to send spam messages to lured users, spammers creating fake profiles, leading to fraud or malware campaigns. Sometimes to send malicious messages, cybercriminals use stolen accounts of legitimate users. Nowadays they are creating short URLs by the short URL service provider and posted on to friends board. Lured users unknowingly clicking on these links, then they are redirected to malicious websites. To control such type of activi
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Ramalho, Miguel António Palma dos Santos Sozinho. "High-level Approaches to Detect Malicious Political Activity on Twitter." Master's thesis, 2020. https://hdl.handle.net/10216/128528.

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Ramalho, Miguel António Palma dos Santos Sozinho. "High-level Approaches to Detect Malicious Political Activity on Twitter." Dissertação, 2020. https://hdl.handle.net/10216/128528.

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Yang, Tzu-Hsuan, and 楊子萲. "Analyzing Social Network Data Using Deep Neural Networks: A Case Study Using Twitter Posts." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/wtqyh3.

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碩士<br>國立政治大學<br>資訊科學系<br>107<br>Interaction on various social networking platforms has become an important part of our daily life. Apart from text messages, image is also a popular media format utilized for online communication. Text or image alone, however, cannot fully convey the ideas that users wish to express. In the thesis, we employ computer vision and word embedding techniques to analyze the relationship between image content and text messages and explore the rich information entangled. The limitation on the total number of characters compels Twitter users to compose their messages mor
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"A Critical Discourse Analysis of Twitter Posts on The Perspectives of Women Driving in Saudi Arabia." Master's thesis, 2017. http://hdl.handle.net/2286/R.I.45498.

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abstract: The issue of women driving remains to be highly debated in Saudi Arabia. Recent developments on its legalization have sparked conversation and discourse, particularly in social media sites like Twitter. Several hashtags have been used to indicate either support or criticism towards the movement. Examining Twitter tweets and hashtags, the study explored how the discourse on women driving had been executed, particularly in between genders. The study analyzed a sizeable number of tweets as well as their context via linguistic corpora analysis. Following Norman Fairclough’s framework, t
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Books on the topic "Malicious posts on Twitter"

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Rogers, Richard. The Propagation of Misinformation in Social Media. Amsterdam University Press, 2023. http://dx.doi.org/10.5117/9789463720762.

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There is growing awareness about how social media circulate extreme viewpoints and turn up the temperature of public debate. Posts that exhibit agitation garner disproportionate engagement. Within this clamour, fringe sources and viewpoints are mainstreaming, and mainstream media are marginalized. This book takes up the mainstreaming of the fringe and the marginalization of the mainstream. In a cross-platform analysis of Google Web Search, Facebook, YouTube, Reddit, Twitter, Instagram, 4chan and TikTok, we found that hyperpartisan web operators, alternative influencers and ambivalent commentat
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Radians, George. Twitter Posts Planner: Organizer to Plan All Your Posts and Content. Lulu Press, Inc., 2021.

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Gray, Davina. Twitter Posts Planner: Organizer to Plan All Your Posts and Content. Lulu Press, Inc., 2021.

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Gray, Davina. Twitter Posts Planner: Organizer to Plan All Your Posts and Content. Lulu Press, Inc., 2021.

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Gray, Davina. Twitter Posts Planner: Organizer to Plan All Your Posts and Content. Lulu Press, Inc., 2021.

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Radians, George. Twitter Posts Planner: Organizer to Plan All Your Posts and Content. Lulu Press, Inc., 2021.

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Radians, George. Twitter Posts Planner: Organizer to Plan All Your Posts and Content. Lulu Press, Inc., 2021.

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Radians, George. Twitter Posts Planner: Organizer to Plan All Your Posts and Content. Lulu Press, Inc., 2021.

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Radians, George. Twitter Posts Planner: Organizer to Plan All Your Posts and Content. Lulu Press, Inc., 2021.

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Gray, Davina. Twitter Posts Planner: Organizer to Plan All Your Posts and Content. Lulu Press, Inc., 2021.

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Book chapters on the topic "Malicious posts on Twitter"

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Chiroma, Fatima, Mihaela Cocea, and Han Liu. "Detection of Suicidal Twitter Posts." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29933-0_26.

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Amato, Flora, Giovanni Cozzolino, Antonino Mazzeo, and Sara Romano. "Malicious Event Detecting in Twitter Communities." In Smart Innovation, Systems and Technologies. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39345-2_6.

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Azeez, Nureni Ayofe, Oluwadamilola Atiku, Sanjay Misra, Adewole Adewumi, Ravin Ahuja, and Robertas Damasevicius. "Detection of Malicious URLs on Twitter." In Lecture Notes in Electrical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5558-9_29.

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Friginal, Eric, Oksana Waugh, and Ashley Titak. "Linguistic Variation in Facebook and Twitter Posts." In Studies in Corpus-Based Sociolinguistics. Routledge, 2017. http://dx.doi.org/10.4324/9781315527819-15.

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Satija, Tanu, and Nirmalya Kar. "Detecting Malicious Twitter Bots Using Machine Learning." In Advances in Computational Intelligence, Security and Internet of Things. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3666-3_16.

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Chen, Zhouhan, Rima S. Tanash, Richard Stoll, and Devika Subramanian. "Hunting Malicious Bots on Twitter: An Unsupervised Approach." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67256-4_40.

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Wang, Xiaofeng, Matthew S. Gerber, and Donald E. Brown. "Automatic Crime Prediction Using Events Extracted from Twitter Posts." In Social Computing, Behavioral - Cultural Modeling and Prediction. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29047-3_28.

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Kajanan, Sangaralingam, Ahmed Shafeeq Bin Mohd Shariff, Kaushik Dutta, and Anindya Datta. "Resolving Name Conflicts for Mobile Apps in Twitter Posts." In Shaping the Future of ICT Research. Methods and Approaches. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35142-6_1.

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Adekotujo, Akinlolu Solomon, JooYoung Lee, Ayokunle Oluwatoyin Enikuomehin, Manuel Mazzara, and Segun Benjamin Aribisala. "Bi-lingual Intent Classification of Twitter Posts: A Roadmap." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14687-0_1.

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Bharany, Salil, Shadab Alam, Mohammed Shuaib, and Bhanu Talwar. "Sentiment Analysis of Twitter Data for COVID-19 Posts." In Data Intelligence and Cognitive Informatics. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6004-8_37.

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Conference papers on the topic "Malicious posts on Twitter"

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Rahman, Hilmy, Yoggy Harisusilo Putra, Hisyam Syarif, Erick Delenia, Yulian Findawati, and Diana Purwitasari. "Dangerous Speech in Indonesian Twitter Posts: A Literature Review." In 2024 International Conference on Information Technology Research and Innovation (ICITRI). IEEE, 2024. http://dx.doi.org/10.1109/icitri62858.2024.10698923.

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Bhale, Kanchan, and Thirupurasundari D. R. "Malicious Social Bot Detection in Twitter: A Review." In 2024 4th International Conference on Computer, Communication, Control & Information Technology (C3IT). IEEE, 2024. https://doi.org/10.1109/c3it60531.2024.10829441.

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Kurnianingrum, Dian, Isma Addi Jumbri, Mila Andria Savitri, Nugraha Nugraha, Disman Disman, and Rachel Monica. "Decoding Financial Positivity: Sentiment Analysis of Mandiri Sekuritas's Twitter Posts." In 2024 6th International Conference on Cybernetics and Intelligent System (ICORIS). IEEE, 2024. https://doi.org/10.1109/icoris63540.2024.10903727.

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Divani, N., and A. Vinitha. "Machine Learning-Based Detection of Malicious URLs in Twitter." In 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS). IEEE, 2025. https://doi.org/10.1109/icmlas64557.2025.10968493.

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Acharya, Malika, and Shilpi Sharma. "Semantic Analysis of Twitter Posts." In 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS). IEEE, 2018. http://dx.doi.org/10.1109/ctems.2018.8769302.

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Bates, Andrew, and Jugal Kalita. "Counting Clusters in Twitter Posts." In the Second International Conference. ACM Press, 2016. http://dx.doi.org/10.1145/2905055.2905295.

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Wani, Kalpana, Aditya Patil, Sumedha Mukherjee, and Sushmita Sarkar. "Malicious Twitter Bot Detector." In 2021 4th Biennial International Conference on Nascent Technologies in Engineering (ICNTE). IEEE, 2021. http://dx.doi.org/10.1109/icnte51185.2021.9487674.

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Minot, Ariana S., Andrew Heier, Davis King, Olga Simek, and Nicholas Stanisha. "Searching for Twitter Posts by Location." In ICTIR '15: ACM SIGIR International Conference on the Theory of Information Retrieval. ACM, 2015. http://dx.doi.org/10.1145/2808194.2809480.

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Wührl, Amelie, and Roman Klinger. "Claim Detection in Biomedical Twitter Posts." In Proceedings of the 20th Workshop on Biomedical Language Processing. Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.bionlp-1.15.

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Hu, Pengwei, Chenhao Lin, Hui Su, et al. "BlueMemo: Depression Analysis through Twitter Posts." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/760.

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The use of social media runs through our lives, and users' emotions are also affected by it. Previous studies have reported social organizations and psychologists using social media to find depressed patients. However, due to the variety of content published by users, it isn't effortless for the system to consider the text, image, and even the hidden information behind the image. To address this problem, we proposed a new system for social media screening of depressed patients named BlueMemo. We collected real-time posts from Twitter. Based on the posts, learned text features, image features,
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Reports on the topic "Malicious posts on Twitter"

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Meisser, Amelia. The Benefits and Drawbacks of Physical Activity According to Social Media: A Content Analysis of Celebrities' Posts on Twitter and Instagram. Portland State University Library, 2015. http://dx.doi.org/10.15760/honors.164.

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Graham, Timothy, and Katherine M. FitzGerald. Bots, Fake News and Election Conspiracies: Disinformation During the Republican Primary Debate and the Trump Interview. Queensland University of Technology, 2023. http://dx.doi.org/10.5204/rep.eprints.242533.

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We used Alexandria Digital, a world leading disinformation detection technology, to analyse almost a million posts on X (formerly known as Twitter) and Reddit comments during the first Republican primary debate and counterprogrammed Tucker Carlson and Donald Trump interview on the 23rd of August. What we did: • Collected 949,259 posts from the platform X, formerly known as Twitter. These posts were collected if they contained one of 11 relevant hashtags or keywords and were posted between 8:45pm and 11:15pm EST on 23rd August 2023. • Collected 20,549 comments from two separate Reddit threads.
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Etu, Egbe-Etu, Asha Weinstein Agrawal, Imokhai Tenebe, Jordan Larot, and Dang Minh Nhu Nguyen. Misconduct on Public Transit: An Exploratory Analysis Using the Comments Formerly Known as Tweets. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2317.

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This project developed a simple methodology for using Twitter data to explore public perceptions about misconduct on public transit in California. The methodology allows future researchers to analyze tweets to answer questions such as: How frequent are tweets related to assault, abuse, or other misconduct on public transit? What concerns arise most frequently? What are the types of behaviors discussed? We collected and analyzed data from Twitter posts in California about various types of public transit misconduct from January 2020 to March 2023 to identify the nature and frequency of reported
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Warren, Nancy, Pia Mingkwan, Caroline Kery, Meagan Meekins, Thomas Bukowski, and Laura Nyblade. Identifying and Classifying COVID-19 Stigma on Social Media. RTI Press, 2023. http://dx.doi.org/10.3768/rtipress.2023.op.0087.2305.

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Since the introduction of COVID-19 in early 2020, COVID-19 stigma has persisted on social media. Stigma, a social process where individuals or groups are labeled, stereotyped, and separated, can result in misinformation, discrimination, and violence. The body of research on COVID-19 stigma is growing, but addressing stigma on social media remains challenging because of the enormous volume and diversity of rapidly changing content. This three-part methodology offers a standardized approach for generating (1) a relevant and manageable social media sample for stigma identification and research, (
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Droogan, Julian, Lise Waldek, Brian Ballsun-Stanton, and Jade Hutchinson. Mapping a Social Media Ecosystem: Outlinking on Gab & Twitter Amongst the Australian Far-right Milieu. RESOLVE Network, 2022. http://dx.doi.org/10.37805/remve2022.6.

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Attention to the internet and the online spaces in which violent extremists interact and spread content has increased over the past decades. More recently, that attention has shifted from understanding how groups like the self-proclaimed Islamic State use the internet to spread propaganda to understanding the broader internet environment and, specifically, far-right violent extremist activities within it. This focus on how far right violent extremist—including far-right racially and ethnically motivated violent extremists (REMVEs) within them—create, use, and exploit the online networks in whi
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