demonstration

On-Demand Bot Detection and Archival System

Authors:

Nikan Chavoshi,

Hossein Hamooni,

Abdullah MueenAuthors Info & Claims

WWW '17 Companion: Proceedings of the 26th International Conference on World Wide Web Companion

Pages 183 - 187

https://doi.org/10.1145/3041021.3054733

Published: 03 April 2017 Publication History

Abstract

Unusually high correlation in activities among users in social media is an indicator of bot behavior. We have developed a system, called DeBot, that identifies such bots in Twitter network. Our system reports and archives thousands of bot accounts every day. DeBot is an unsupervised method capable of detecting bots in a parameter-free fashion. In February 2017, DeBot has collected over 710K unique bots since August 2015. Since we are detecting and archiving Twitter bots on a daily basis, we have the ability to offer two different services based on our bot detection system. The first one is a bot archive API that makes it easy for researchers to query the DeBot's archive. This API can be used to answer various queries: Is a given Twitter account a bot? When was this bot active in the past? Which twitter accounts were detected as bots on a specific date? The second service that we offer is an on-demand bot detection platform which can detect bots that are related to a given topic or geographical location, and report them to the user in few hours. This paper explains all the details of the services we offer on top of the DeBot's bot detection engine.

References

[1]

A video screen capture of our services. http://www.cs.unm.edu/~chavoshi/debot/screencast.html.

[2]

Bot or not? a truthy project. http://truthy.indiana.edu/botornot/.

[3]

Debot api on github. https://github.com/nchavoshi/debot_api.

[4]

Supporting Page -- Supporting webpage containing video, data, code and daily report. www.cs.unm.edu/~chavoshi/debot.

[5]

Twitter Streaming API. https://dev.twitter.com/streaming/overview.

[6]

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Digital Library

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N. Chavoshi, H. Hamooni, and A. Mueen. Debot: Twitter bot detection via warped correlation. In IEEE International Conference on Data Mining (ICDM), 2016.

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N. Chavoshi, H. Hamooni, and A. Mueen. Identifying correlated bots in twitter. In Social Informatics - 8th International Conference, SocInfo 2016, Bellevue, WA, USA, November 11-14, 2016, Proceedings, Part II, pages 14--21, 2016.

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K. Thomas, V. Paxson, D. Mccoy, and C. Grier. Trafficking Fraudulent Accounts: The Role of the Underground Market in Twitter Spam and Abuse Trafficking Fraudulent Accounts. In USENIX Security Symposium, pages 195--210, 2013.

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Cited By

Alarfaj FAhmad HKhan HAlomair AAlmusallam NAhmed M(2023)Twitter Bot Detection Using Diverse Content Features and Applying Machine Learning AlgorithmsSustainability10.3390/su1508666215:8(6662)Online publication date: 14-Apr-2023
https://doi.org/10.3390/su15086662
Khan YThakur SObiyemi OAdetiba E(2022)Identification of Bots and Cyborgs in the #FeesMustFall CampaignInformatics10.3390/informatics90100219:1(21)Online publication date: 4-Mar-2022
https://doi.org/10.3390/informatics9010021
Rajendran GRam AVijayan VPoornachandran P(2020)Deep Temporal Analysis of Twitter BotsMachine Learning and Metaheuristics Algorithms, and Applications10.1007/978-981-15-4301-2_4(38-48)Online publication date: 5-Apr-2020
https://doi.org/10.1007/978-981-15-4301-2_4
Show More Cited By

Index Terms

On-Demand Bot Detection and Archival System
1. Information systems
  1. World Wide Web
    1. Web applications
      1. Social networks
    2. Web mining

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Information & Contributors

Information

Published In

cover image ACM Other conferences

WWW '17 Companion: Proceedings of the 26th International Conference on World Wide Web Companion

April 2017

1738 pages

ISBN:9781450349147

General Chairs:
Rick Barrett
W3Events
,
Rick Cummings
Murdoch University
,
Program Chairs:
Eugene Agichtein
Emory University
,
Evgeniy Gabrilovich
Google Research

Sponsors

IW3C2: International World Wide Web Conference Committee

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 03 April 2017

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Demonstration

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WWW '17

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IW3C2

WWW '17: 26th International World Wide Web Conference

April 3 - 7, 2017

Perth, Australia

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WWW '17 Companion Paper Acceptance Rate 164 of 966 submissions, 17%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Alarfaj FAhmad HKhan HAlomair AAlmusallam NAhmed M(2023)Twitter Bot Detection Using Diverse Content Features and Applying Machine Learning AlgorithmsSustainability10.3390/su1508666215:8(6662)Online publication date: 14-Apr-2023
https://doi.org/10.3390/su15086662
Khan YThakur SObiyemi OAdetiba E(2022)Identification of Bots and Cyborgs in the #FeesMustFall CampaignInformatics10.3390/informatics90100219:1(21)Online publication date: 4-Mar-2022
https://doi.org/10.3390/informatics9010021
Rajendran GRam AVijayan VPoornachandran P(2020)Deep Temporal Analysis of Twitter BotsMachine Learning and Metaheuristics Algorithms, and Applications10.1007/978-981-15-4301-2_4(38-48)Online publication date: 5-Apr-2020
https://doi.org/10.1007/978-981-15-4301-2_4
Di Pietro RRaponi SCaprolu MCresci SDi Pietro RRaponi SCaprolu MCresci S(2020)Information DisorderNew Dimensions of Information Warfare10.1007/978-3-030-60618-3_2(7-64)Online publication date: 4-Dec-2020
https://doi.org/10.1007/978-3-030-60618-3_2
Albadi NKurdi MMishra S(2019)Hateful People or Hateful Bots?Proceedings of the ACM on Human-Computer Interaction10.1145/33591633:CSCW(1-25)Online publication date: 7-Nov-2019
https://dl.acm.org/doi/10.1145/3359163
Cresci SLillo FRegoli DTardelli STesconi M(2019)Cashtag PiggybackingACM Transactions on the Web10.1145/331318413:2(1-27)Online publication date: 3-Apr-2019
https://dl.acm.org/doi/10.1145/3313184
Abu-El-Rub NMueen A(2019)BotCamp: Bot-driven Interactions in Social CampaignsThe World Wide Web Conference10.1145/3308558.3313420(2529-2535)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313420
Bello BHeckel R(2019)Analyzing the Behaviour of Twitter Bots in Post Brexit Politics2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS)10.1109/SNAMS.2019.8931874(61-66)Online publication date: Oct-2019
https://doi.org/10.1109/SNAMS.2019.8931874
Saeed ZAbbasi RMaqbool OSadaf ARazzak IDaud AAljohani NXu G(2019)What's Happening Around the World? A Survey and Framework on Event Detection Techniques on TwitterJournal of Grid Computing10.1007/s10723-019-09482-217:2(279-312)Online publication date: 31-Jul-2019
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Daouadi KRebaï RAmous I(2019)Bot Detection on Online Social Networks Using Deep ForestArtificial Intelligence Methods in Intelligent Algorithms10.1007/978-3-030-19810-7_30(307-315)Online publication date: 5-May-2019
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