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Academic literature on the topic 'Retweets cascades'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Retweets cascades"

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Okubo, Kyota, and Kazumasa Oida. "A Successful Advertising Strategy over Twitter." Computer and Information Science 10, no. 3 (2017): 10. http://dx.doi.org/10.5539/cis.v10n3p10.

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Large information cascades over online social networks have attracted a great deal of attention. The life times of most cascades are quite short, whereas recent advertising campaigns sometimes generate long-lived ones by employing effective dissemination strategies (instant-win, reminders, etc.). This paper reports one such campaign, YOGUR STAND, on the Twitter network in Japan. The data analysis shows that the campaign popularity has two interesting features. (1) It shows elastic behavior in that the impact of the destructive earthquake on the popularity was quite temporary. (2) It exhibits s

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Maarouf, Abdurahman, Nicolas Pröllochs, and Stefan Feuerriegel. "The Virality of Hate Speech on Social Media." Proceedings of the ACM on Human-Computer Interaction 8, CSCW1 (2024): 1–22. http://dx.doi.org/10.1145/3641025.

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Online hate speech is responsible for violent attacks such as, e.g., the Pittsburgh synagogue shooting in 2018, thereby posing a significant threat to vulnerable groups and society in general. However, little is known about what makes hate speech on social media go viral. In this paper, we collect N = 25,219 cascades with 65,946 retweets from X (formerly known as Twitter) and classify them as hateful vs. normal. Using a generalized linear regression, we then estimate differences in the spread of hateful vs. normal content based on author and content variables. We thereby identify important det

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Li, Mingxin, Yuchen Zhang, Haowei Xu, Xianghua Li, Chao Gao, and Zhen Wang. "Learning Complex Heterogeneous Multimodal Fake News via Social Latent Network Inference." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 1 (2025): 433–41. https://doi.org/10.1609/aaai.v39i1.32022.

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With the diversification of online social platforms, news dissemination has become increasingly complex, heterogeneous, and multimodal, making the fake news detection task more challenging and crucial. Previous works mainly focus on obtaining social relationships of news via retweets, limiting the accurate detection when real cascades are inaccessible. Given the proven assessment of the spreading influence of events, this paper proposes a method called HML (Complex Heterogeneous Multimodal Fake News Detection method via Latent Network Inference). Specifically, an improved social latent network

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Bastos, Marco T., and Dan Mercea. "The Brexit Botnet and User-Generated Hyperpartisan News." Social Science Computer Review 37, no. 1 (2017): 38–54. http://dx.doi.org/10.1177/0894439317734157.

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In this article, we uncover a network of Twitterbots comprising 13,493 accounts that tweeted the United Kingdom European Union membership referendum, only to disappear from Twitter shortly after the ballot. We compare active users to this set of political bots with respect to temporal tweeting behavior, the size and speed of retweet cascades, and the composition of their retweet cascades (user-to-bot vs. bot-to-bot) to evidence strategies for bot deployment. Our results move forward the analysis of political bots by showing that Twitterbots can be effective at rapidly generating small- to medi

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Kupavskii, Andrey, Alexey Umnov, Gleb Gusev, and Pavel Serdyukov. "Predicting the Audience Size of a Tweet." Proceedings of the International AAAI Conference on Web and Social Media 7, no. 1 (2021): 693–96. http://dx.doi.org/10.1609/icwsm.v7i1.14454.

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We study information dissemination in Twitter. We present an analysis of two important characteristics of so called retweet cascades: retweet count and show count, i.e., number of users that receive the tweet in their feed. We show that these two measures behave differently. We describe three models that are aimed to predict the audience size of a tweet: first one utilizes only the data available at the moment of the initial tweet, the second one utilizes the spread of the cascade up to some moment while the third one is an online prediction.

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Drolsbach, Chiara Patricia, and Nicolas Pröllochs. "Diffusion of Community Fact-Checked Misinformation on Twitter." Proceedings of the ACM on Human-Computer Interaction 7, CSCW2 (2023): 1–22. http://dx.doi.org/10.1145/3610058.

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The spread of misinformation on social media is a pressing societal problem that platforms, policymakers, and researchers continue to grapple with. As a countermeasure, recent works have proposed to employ non-expert fact-checkers in the crowd to fact-check social media content. While experimental studies suggest that crowds might be able to accurately assess the veracity of social media content, an understanding of how crowd fact-checked (mis-)information spreads is missing. In this work, we empirically analyze the spread of misleading vs. not misleading community fact-checked posts on social

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Kobayashi, Ryota, and Renaud Lambiotte. "TiDeH: Time-Dependent Hawkes Process for Predicting Retweet Dynamics." Proceedings of the International AAAI Conference on Web and Social Media 10, no. 1 (2021): 191–200. http://dx.doi.org/10.1609/icwsm.v10i1.14717.

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Online social networking services allow their users to post content in the form of text, images or videos. The main mechanism driving content diffusion is the possibility for users to re-share the content posted by their social connections, which may then cascade across the system. A fundamental problem when studying information cascades is the possibility to develop sound mathematical models, whose parameters can be calibrated on empirical data, in order to predict the future course of a cascade after a window of observation. In this paper, we focus on Twitter and, in particular, on the tempo

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Xia, Yan, Ted Hsuan Yun Chen, and Mikko Kivelä. "Spread of tweets in climate discussions: A case study of the 2019 Nobel Peace Prize announcement." Nordic Journal of Media Studies 3, no. 1 (2021): 96–117. http://dx.doi.org/10.2478/njms-2021-0006.

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Abstract Characterising the spreading of ideas within echo chambers is essential for understanding polarisation. In this article, we explore the characteristics of popular and viral content in climate change discussions on Twitter around the 2019 announcement of the Nobel Peace Prize, where we find the retweet network of users to be polarised into two well-separated groups of activists and sceptics. Operationalising popularity as the number of retweets and virality as the spreading probability inferred using an independent cascade model, we find that the viral themes echo and differ from the p

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Karthick.D and G. Vadivu Dr. "Seismic A Self Exciting Point Process Model for Predicting Tweet Popularity using Hashtags." International Journal of Trend in Scientific Research and Development 1, no. 5 (2017): 796–802. https://doi.org/10.31142/ijtsrd2366.

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In existing paper they had used a full month of Twitter data to evaluate SEISMIC .In which the original data set contains over 3.2 billion tweets and retweets on Twitter from Octobor 7 to November 7, 2011.Also they only kept tweets such that it has at least 50 retweets, the text of the tweet does not contain a pound sign hashtag , and the language of the original poster is English. There are 166,076 tweets satisfying these criteria in the end.So here we are going to propose the mining of tweets with a particular hashtags and going to formulate the number of retweets in an efficient manner ,so

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Zola, Paola, Guglielmo Cola, Michele Mazza, and Maurizio Tesconi. "Interaction Strength Analysis to Model Retweet Cascade Graphs." Applied Sciences 10, no. 23 (2020): 8394. http://dx.doi.org/10.3390/app10238394.

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Tracking information diffusion is a non-trivial task and it has been widely studied across different domains and platforms. The advent of social media has led to even more challenges, given the higher speed of information propagation and the growing impact of social bots and anomalous accounts. Nevertheless, it is crucial to derive a trustworthy information diffusion graph that is capable of highlighting the importance of specific nodes in spreading the original message. The paper introduces the interaction strength, a novel metric to model retweet cascade graphs by exploring users’ interactio

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Book chapters on the topic "Retweets cascades"

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Neumann, Jennifer, and Peter M. Fischer. "Inferring Missing Retweets in Twitter Information Cascades." In New Trends in Database and Information Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85082-1_25.

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Goel, Ashish, Kamesh Munagala, Aneesh Sharma, and Hongyang Zhang. "A Note on Modeling Retweet Cascades on Twitter." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26784-5_10.

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Chunyan, Tong, Zhang Kai, Yang Song, et al. "Context-User Dependent Model for Cascade Retweeter Prediction." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-28124-2_61.

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Conference papers on the topic "Retweets cascades"

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Barbosa, Carlos, Lucas Félix, Vinícius Vieira, and Carolina Xavier. "Sara - A Semi-Automatic Framework for Social Network Analysis." In XXV Simpósio Brasileiro de Sistemas Multimídia e Web. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/webmedia_estendido.2019.8137.

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Understanding the dissemination of information in social networks has become essential for modern societies. These networks have dramatically changed the mode of communication, relationship, marketing, and access to information. Platforms such as Twitter, and WhatsApp are some representatives of these new information propagation media that represented a major shift in a model centered on traditional communication vehicles. This new decentralized environment gave voice to marginalized groups, riots such as the Arab Spring, growth of populist parties and false news waves across the globe. Theref

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Zhao, Yunwei, Can Wang, Chi-Hung Chi, Kwok-Yan Lam, and Sen Wang. "A Comparative Study of Transactional and Semantic Approaches for Predicting Cascades on Twitter." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/169.

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The availability of massive social media data has enabled the prediction of people’s future behavioral trends at an unprecedented large scale. Information cascades study on Twitter has been an integral part of behavior analysis. A number of methods based on the transactional features (such as keyword frequency) and the semantic features (such as sentiment) have been proposed to predict the future cascading trends. However, an in-depth understanding of the pros and cons of semantic and transactional models is lacking. This paper conducts a comparative study of both approaches in predicting info

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Kupavskii, Andrey, Liudmila Ostroumova, Alexey Umnov, et al. "Prediction of retweet cascade size over time." In the 21st ACM international conference. ACM Press, 2012. http://dx.doi.org/10.1145/2396761.2398634.

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Yang, Song, Zheng Zhang, Hao Jiang, Cong Zheng, and Xiaoyue Zhao. "Topic-Aware Popularity and Retweeter Prediction Model for Cascade Study." In 2022 IEEE 9th International Conference on Cyber Security and Cloud Computing (CSCloud)/2022 IEEE 8th International Conference on Edge Computing and Scalable Cloud (EdgeCom). IEEE, 2022. http://dx.doi.org/10.1109/cscloud-edgecom54986.2022.00038.

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Zhang, Xinmiao, and Gan Wang. "Application of Information Diffusion of Negative Emotion and Binomial Regression Method on Retweet Cascades during Covid-19." In 2021 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA). IEEE, 2021. http://dx.doi.org/10.1109/aeeca52519.2021.9574300.

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