skip to main content
10.1145/3106426.3106483acmconferencesArticle/Chapter ViewAbstractPublication PageswiConference Proceedingsconference-collections
research-article

Identifying active, reactive, and inactive targets of socialbots in Twitter

Published: 23 August 2017 Publication History

Abstract

Online social networks are facing serious threats due to presence of human-behaviour imitating malicious bots (aka socialbots) that are successful mainly due to existence of their duped followers. In this paper, we propose an approach to categorize Twitter users into three groups - active, reactive, and inactive targets, based on their interaction behaviour with socialbots. Active users are those who themselves follow socialbots without being followed by them, reactive users respond to the following socialbots by following them back, whereas inactive users do not show any interest against the following requests from anonymous socialbots. The proposed approach is modelled as both binary and ternary classification problem, wherein users' profile is generated using static and dynamic components representing their identical and behavioural aspects. Three different classification techniques viz Naive Bayes, Reduced Error Pruned Decision Tree, and Random Forest are used over a dataset of 749 users collected through live experiment, and a thorough analyses of the identified users categories is presented, wherein it is found that active and reactive users keep on frequently updating their tweets containing advertising related contents. Finally, feature ranking algorithms are used to rank identified features to analyse their discriminative power, and it is found that following rate and follower rate are the most dominating features.

References

[1]
Gediminas Adomavicius and Alexander Tuzhilin. 1999. User Profiling in Personalization Applications through Rule Discovery and Validation. In Proceedings of the 5th International Conference on Knowledge Discovery and Data Mining. ACM, San Diego, USA, 377--381.
[2]
Faraz Ahmad and Muhammad Abulaish. June 25--27, 2012. An MCL-Based Approach for Spam Profile Detection in Online Social Networks. In Proceedings of the 11th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE-TrustCom). IEEE Computer Society, Liverpool, UK, 602--608.
[3]
Faraz Ahmed and Muhammad Abulaish. 2013. A Generic Statistical Approach for Spam Detection in Online Social Networks. Computer Communications 36, 10--11 (2013), 1120--1129.
[4]
Luca Maria Aiello, Martina Deplano, Rossano Schifanella, and Giancarlo Ruffo. 2012. People are Strange When You're a Stranger: Impact and Influence of Bots on Social Networks. In Proceedings of the 6th International Conference on Weblogs and Social Media. AAAI Press, Dublin, Ireland, 10--17.
[5]
Nutan Reddy Amit A Amleshwaram, Suneel Yadav, Guofei Gu, and Chao Yang. 2013. CATS: Characterizing Automation of Twitter Spammers. In Proceedings of the 5th International Conference on Communication Systems and Networks (COMSNETS). IEEE Computer Society, Banglore, India, 1--10.
[6]
Sajid Y. Bhat and Muhammad Abulaish. 2014. Communities against Deception in Online Social Networks. Computer Fraud and Security 2014, 2 (2014), 8--16.
[7]
Yazan Boshmaf, Ildar Muslukhov, Konstantin Beznosov, and Matei Ripeanu. 2011. The Socialbot Network: When Bots Socialize for Fame and Money. In Proceedings of the 27th Annual Computer Security Applications Conference. ACM, Orlando, Florida USA, 93--102.
[8]
Yazan Boshmaf, Ildar Muslukhov, Konstantin Beznosov, and Matei Ripeanu. 2013. Design and Analysis of Social Botnet. Computer Networks 57, 2 (2013), 556--578.
[9]
Yazan Boshmaf, Matei Ripeanu, Konstantin Beznosov, and Elizeu Santos-Neto. 2015. Thwarting Fake OSN Accounts by Predicting their Victims. In Proceedings of the 8th Workshop on Artificial Intelligence and Security. ACM, Denver, USA, 81--89.
[10]
Zi Chu, Steven Gianvecchio, Haining Wang, and Sushil Jajodia. 2010. Who is Tweeting on Twitter: Human, Bot, or Cyborg?. In Proceedings of the 26th Annual Computer Security Applications Conference. ACM, Austin, Texas, USA, 21--30.
[11]
Zi Chu, Steven Gianvecchio, Haining Wang, and Sushil Jajodia. 2012. Detecting Automation of Twitter Accounts: Are You a Human, Bot, or Cyborg? IEEE Transactions on Dependable and Secure Computing 9, 6 (2012), 811--824.
[12]
Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, and Maurizio Tesconi. 2016. DNA-Inspired Online Behavioral Modeling and Its Application to Spambot Detection. IEEE Intelligent System 31,5 (2016), 58--64.
[13]
Aviad Elyashar, Michael Fire, Dima Kagan, and Yuval Elovici. 2013. Homing Socialbots: Intrusion on a Specific Organization's Employee using Socialbots. In Proceedings of the International Conference on Advances in Social Networks Analysis and Mining. IEEE Computer Society/ACM, Niagara Falls, Canada, 1358--1365.
[14]
Sandra Garcia Esparza, Michael P. OfiMahony, and Barry Smyth. 2013. CatStream: Categorising Tweets for User Profiling and Stream Filtering. In Proceedings of the International Conference on Intelligent User Interfaces. ACM, Santa Monica, CA, USA, 25--36.
[15]
Mohd Fazil and Muhammad Abulaish. 2017. Why a Socialbot is Effective in Twitter? A Statistical Insight. In Proceedings of the 9th International Conference on Communication Systems and Networks (COMSNETS), Social Networking Workshop. IEEE Computer Society, Bengaluru, India, 562--567.
[16]
Lewis R Goldberg. 1993. The Structure of Phenotypic Personality Traits. American Psychologist 48, 1 (1993), 26--34.
[17]
Mark A. Hall. 1999. Correlation-based Feature Selection for Machine Learning. Ph.D. Dissertation. The University of Waikato, New Zealand.
[18]
Igor Kononenko. 1994. Estimating Attributes: Analysis and Extensions of RELIEF. In Proceedings of the European Conference on Machine Learning. Springer, Berlin, Heidelberg, Italy, 171--182.
[19]
Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon. 2010. What is Twitter, a Social Network or a News Media?. In Proceedings of the 19th International Conference on World Wide Web. ACM, Raleigh, North Carolina, USA, 591--600.
[20]
Kyumin Lee, Brian David Eoff, and James Caverlee. 2011. Seven Months with the Devils: A Long-Term Study of Content Polluters on Twitter. In Proceedings of the 5th International Conference on Weblogs and Social Media. ACM, Santa Monica, CA, USA, 185--192.
[21]
Tom Mitchell. 1997. Machine Learning. McGraw Hill.
[22]
Richard J. Oentaryo, Arinto Murdopo, Philips K. Prasetyo, and Ee-Peng Lim. 2016. On Profiling Bots in Social Media. In Proceedings of the International Conference on Social Informatics. Springer, Bellevue, WA, USA, 92--109.
[23]
Marco Pennacchioti and Ana-Maria Popescu. 2011. A Machine Learning Approach to Twitter User Classification. In Proceedings of the 5th International Conference on Weblogs and Social Media. AAAI Press, Barcelona, Spain, 281--288.
[24]
Muhammad Z. Rafique and Muhammad Abulaish. August 27--31, 2012. Graph-Based Learning Model for Detection of SMS Spam on Smart Phones. In Proceedings of the 8th International Wireless Communications and Mobile Computing Conference (IWCMC'12) fi Trust, Privacy and Security Symposium. IEEE Computer Society, Limasol, Cyprus, 27--31.
[25]
Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano, and Chris Sumner. 2013. Which Users Reply to and Interact with Twitter Social Bots?. In Proceedings of the 25th International Conference on Tools with Artificial Intelligence. IEEE Computer Society, Herndon, VA, USA, 135--144.
[26]
Chao Yang, Robert Harkreader, and Guofei Gu. 2013. Empirical Evaluation and New Design for Fighting Evolving Twitter Spammers. IEEE Transactions on Information Forensics and Security 8, 8 (2013), 1280--1293.
[27]
Jinxue Zhang, Rui Zhang, Yanchao Zhang, and Guanhua Yan. 2012. On the Impact of Social Botnets for Spam Distribution and Digital Influence Manipulation. In Proceedings of the 6th International Conference on Communications and Network Security. IEEE Communications Society, National Harbor, MD, USA, 46--54.

Cited By

View all
  • (2024)Unsupervised Social Bot Detection via Structural Information TheoryACM Transactions on Information Systems10.1145/366052242:6(1-42)Online publication date: 19-Aug-2024
  • (2024)Deep Learning and Machine Learning-Based Approaches to Inferring Social Media Network Users’ Interests from a Missing Data IssuesKnowledge Science, Engineering and Management10.1007/978-981-97-5489-2_12(134-143)Online publication date: 27-Jul-2024
  • (2022)Tweet, like, subscribe! Understanding leadership through social media useThe Leadership Quarterly10.1016/j.leaqua.2021.10158033:1(101580)Online publication date: Feb-2022
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
WI '17: Proceedings of the International Conference on Web Intelligence
August 2017
1284 pages
ISBN:9781450349512
DOI:10.1145/3106426
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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 August 2017

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Twitter data analysis
  2. social network analysis
  3. socialbot characterization
  4. socialbot identification
  5. user profiling

Qualifiers

  • Research-article

Conference

WI '17
Sponsor:

Acceptance Rates

WI '17 Paper Acceptance Rate 118 of 178 submissions, 66%;
Overall Acceptance Rate 118 of 178 submissions, 66%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)6
  • Downloads (Last 6 weeks)1
Reflects downloads up to 15 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Unsupervised Social Bot Detection via Structural Information TheoryACM Transactions on Information Systems10.1145/366052242:6(1-42)Online publication date: 19-Aug-2024
  • (2024)Deep Learning and Machine Learning-Based Approaches to Inferring Social Media Network Users’ Interests from a Missing Data IssuesKnowledge Science, Engineering and Management10.1007/978-981-97-5489-2_12(134-143)Online publication date: 27-Jul-2024
  • (2022)Tweet, like, subscribe! Understanding leadership through social media useThe Leadership Quarterly10.1016/j.leaqua.2021.10158033:1(101580)Online publication date: Feb-2022
  • (2021)Zombie Follower Recognition Based on Industrial Chain Feature AnalysisSecurity and Communication Networks10.1155/2021/66564702021Online publication date: 1-Jan-2021
  • (2020)A machine learning approach for socialbot targets detection on twitterJournal of Intelligent & Fuzzy Systems10.3233/JIFS-200682(1-19)Online publication date: 7-Nov-2020
  • (2020)A socialbots analysis-driven graph-based approach for identifying coordinated campaigns in twitterJournal of Intelligent & Fuzzy Systems10.3233/JIFS-182895(1-17)Online publication date: 3-Feb-2020
  • (2020)Exploring the construction and infiltration strategies of social bots in sina microblogScientific Reports10.1038/s41598-020-76814-810:1Online publication date: 13-Nov-2020
  • (2019)Detecting Malicious Social Bots based on Clickstream SequencesIEEE Access10.1109/ACCESS.2019.2901864(1-1)Online publication date: 2019
  • (2018)The EthnobotProceedings of the 2018 CHI Conference on Human Factors in Computing Systems10.1145/3173574.3174178(1-13)Online publication date: 21-Apr-2018
  • (2017)A Novel Weighted Distance Measure for Multi-Attributed GraphProceedings of the 10th Annual ACM India Compute Conference10.1145/3140107.3140114(39-47)Online publication date: 16-Nov-2017

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media