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Who Will Retweet This? Detecting Strangers from Twitter to Retweet Information

Authors:

Jeffrey NicholsAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 6, Issue 3

Article No.: 31, Pages 1 - 25

https://doi.org/10.1145/2700466

Published: 21 April 2015 Publication History

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Abstract

There has been much effort on studying how social media sites, such as Twitter, help propagate information in different situations, including spreading alerts and SOS messages in an emergency. However, existing work has not addressed how to actively identify and engage the right strangers at the right time on social media to help effectively propagate intended information within a desired time frame. To address this problem, we have developed three models: (1) a feature-based model that leverages people's exhibited social behavior, including the content of their tweets and social interactions, to characterize their willingness and readiness to propagate information on Twitter via the act of retweeting; (2) a wait-time model based on a user's previous retweeting wait times to predict his or her next retweeting time when asked; and (3) a subset selection model that automatically selects a subset of people from a set of available people using probabilities predicted by the feature-based model and maximizes retweeting rate. Based on these three models, we build a recommender system that predicts the likelihood of a stranger to retweet information when asked, within a specific time window, and recommends the top-N qualified strangers to engage with. Our experiments, including live studies in the real world, demonstrate the effectiveness of our work.

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Zeshan Shabbir MNaseer IAkhter SAbubakar MIssa GHassaan Mehmood M(2023)Fake Twitter Followers Detection using Machine Learning Approach2023 International Conference on Business Analytics for Technology and Security (ICBATS)10.1109/ICBATS57792.2023.10111260(1-7)Online publication date: 7-Mar-2023
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Turkoglu SMutlu A(2023)Improving Retweet Prediction via Tweet Features2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)10.1109/HORA58378.2023.10156692(1-5)Online publication date: 8-Jun-2023
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Wang SYang WLi Z(2023)Towards fake news refuter identification: Mixture of Chi-Merge grounded CNN approachExpert Systems with Applications10.1016/j.eswa.2023.120712231(120712)Online publication date: Nov-2023
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Index Terms

Who Will Retweet This? Detecting Strangers from Twitter to Retweet Information
1. Human-centered computing
  1. Human computer interaction (HCI)

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Reviews

Reviewer: Susan Loretta Fowler

The authors of this paper are from Google, IBM Research, and Utah State University. These are smart folks, and the study shows it. Their premise was that one could predict, based on tweeters past behavior and how they used language, who would be most likely to retweet public service messages. These information propagators are more likely to be willing and ready (within a set time frame) to spread information on request. The authors used two tweets, one location based and the other topic based, for their experiment. The location-based public safety tweet reported a shooting in the San Francisco Bay Area, and the topic-based bird flu tweet said that bird flu was expected to evolve in nature. Both tweets were obtained from news media sites. Their retweeting system succeeded when they sent tweets to people with these characteristics: They have tweeted on the topic before. They have many followers (100 was the bottom threshold). They retweeted within a set period of time (possibly six or 12 hours, although the optimal time frame is not stated). Retweeters word usage in their own tweets tended toward inclusiveness, conscientiousness, and openness on the Big5 and Linguistic Inquiry and Word Count scales. The authors provide copious information about the statistical analyses in which they engaged, so anyone with an interest in the project should be able to reproduce the experiment. At the end of the paper, the authors state: We found that our approaches were able to at least double the retweeting rates over two baselines. With our time estimation model, our approach also outperformed other approaches significantly by achieving a much higher retweeting rate within a given time window. ... In a live setting, our approach consistently outperformed the two baselines by almost doubling their retweeting rates. Overall, our approach effectively identifies qualified candidates for retweeting a message within a given time window. Emergency management groups could really use a system like this. However, there is no uniform resource locator (URL) or other information about how to access the authors system (which was funded by the US Defense Advanced Research Projects Agency, or DARPA). Nevertheless, I suppose that any smart information technology (IT) person would be able to reproduce the system from the paper. Online Computing Reviews Service

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 6, Issue 3

Survey Paper, Regular Papers and Special Section on Participatory Sensing and Crowd Intelligence

May 2015

319 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2764959

Editor:
Huan Liu
Arizona State University, USA

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 April 2015

Accepted: 01 September 2014

Revised: 01 August 2014

Received: 01 March 2014

Published in TIST Volume 6, Issue 3

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https://doi.org/10.1109/ICBATS57792.2023.10111260
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https://doi.org/10.1109/HORA58378.2023.10156692
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https://doi.org/10.1016/j.eswa.2023.120712
Jerez-Villota EJurado FMoreno-Llorena J(2023)Understanding the Role of the User in Information Propagation on Online Social Networks: A Literature Review and Proposed User ModelProceedings of the 15th International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2023)10.1007/978-3-031-48642-5_31(304-315)Online publication date: 26-Nov-2023
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