short-paper

Worth its Weight in Likes: Towards Detecting Fake Likes on Instagram

Authors:

Anupama Aggarwal,

Siddharth Singh,

Ponnurangam Kumaraguru,

Anwitaman DattaAuthors Info & Claims

WebSci '18: Proceedings of the 10th ACM Conference on Web Science

Pages 205 - 209

https://doi.org/10.1145/3201064.3201105

Published: 15 May 2018 Publication History

Abstract

Instagram is a significant platform for users to share media; reflecting their interests. It is used by marketers and brands to reach their potential audience for advertisement. The number of likes on posts serves as a proxy for social reputation of the users, and in some cases, social media influencers with an extensive reach are compensated by marketers to promote products. This emerging market has led to users artificially bolstering the likes they get to project an inflated social worth. In this study, we enumerate the potential factors which contribute towards a genuine like on Instagram. Based on our analysis of liking behaviour, we build an automated mechanism to detect fake likes on Instagram which achieves a high precision of 83.5%. Our work serves an important first step in reducing the effect of fake likes on Instagram influencer market.

References

[1]

Prudhvi Ratna Badri Satya, Kyumin Lee, Dongwon Lee, Thanh Tran, and Jason Jiasheng Zhang . 2016. Uncovering fake likers in online social networks. ACM International Conference on Information and Knowledge Management.

Digital Library

[2]

Saeideh Bakhshi, David A Shamma, and Eric Gilbert . 2014. Faces engage us: Photos with faces attract more likes and comments on instagram ACM Conference on Human factors in Computing Systems.

Digital Library

[3]

Fabricio Benevenuto, Gabriel Magno, Tiago Rodrigues, and Virgilio Almeida . 2010. Detecting spammers on twitter. In Annual Collaboration, Electronic Messaging, Anti-Abuse and Spam Conference.

[4]

Alex Beutel, Wanhong Xu, Venkatesan Guruswami, Christopher Palow, and Christos Faloutsos . 2013. Copycatch: stopping group attacks by spotting lockstep behavior in social networks International Conference on World Wide Web.

Digital Library

[5]

Qiang Cao, Xiaowei Yang, Jieqi Yu, and Christopher Palow . 2014. Uncovering large groups of active malicious accounts in online social networks ACM SIGSAC Conference on Computer and Communications Security.

Digital Library

[6]

Meeyoung Cha, Hamed Haddadi, Fabricio Benevenuto, and P Krishna Gummadi . 2010. Measuring user influence in twitter: The million follower fallacy International AAAI Conference on Web and Social Media.

[7]

Emiliano De Cristofaro, Arik Friedman, Guillaume Jourjon, Mohamed Ali Kaafar, and M Zubair Shafiq . 2014. Paying for likes?: Understanding facebook like fraud using honeypots ACM SIGCOMM conference on Internet Measurement.

Digital Library

[8]

Hongyu Gao, Jun Hu, Christo Wilson, Zhichun Li, Yan Chen, and Ben Y Zhao . 2010. Detecting and characterizing social spam campaigns ACM SIGCOMM Conference on Internet Measurement.

Digital Library

[9]

Maria Giatsoglou, Despoina Chatzakou, Neil Shah, Christos Faloutsos, and Athena Vakali . 2015. Retweeting Activity on Twitter: Signs of Deception Pacific-Asia Conference on Knowledge Discovery and Data Mining.

[10]

Yuheng Hu, Lydia Manikonda, and Subbarao Kambhampati . 2014. What We Instagram: A First Analysis of Instagram Photo Content and User Types International AAAI Conference on Web and Social Media.

[11]

Jin Yea Jang, Kyungsik Han, and Dongwon Lee . 2015. No reciprocity in liking photos: Analyzing like activities in Instagram ACM Conference on Hypertext & Social Media.

Digital Library

[12]

Nitin Jindal and Bing Liu . 2008. Opinion spam and analysis. In International Conference on Web Search and Data Mining.

Digital Library

[13]

Justin Johnson, Andrej Karpathy, and Li Fei-Fei . 2016. Densecap: Fully convolutional localization networks for dense captioning IEEE Conference on Computer Vision and Pattern Recognition.

[14]

Kyumin Lee, James Caverlee, and Steve Webb . 2010. Uncovering social spammers: social honeypots

[15]

machine learning International ACM SIGIR Conference on Research and Development in Information Retrieval.

[16]

Yixuan Li, Oscar Martinez, Xing Chen, Yi Li, and John E Hopcroft . 2016. In a world that counts: Clustering and detecting fake social engagement at scale International Conference on World Wide Web.

Digital Library

[17]

Rada Mihalcea and Andras Csomai . 2007. Wikify!: linking documents to encyclopedic knowledge ACM International Conference on Information and Knowledge Management.

Digital Library

[18]

Tomas Mikolov, Wen-tau Yih, and Geoffrey Zweig . 2013. Linguistic regularities in continuous space word representations North American Chapter of the Association for Computational Linguistics: Human Language Technologies.

[19]

Aditya Pal, Amacc Herdagdelen, Sourav Chatterji, Sumit Taank, and Deepayan Chakrabarti . 2016. Discovery of topical authorities in instagram. In International Conference on World Wide Web.

Digital Library

[20]

Ramine Tinati, Aastha Madaan, and Wendy Hall . 2017. InstaCan: Examining Deleted Content on Instagram. ACM Conference on Web Science.

Digital Library

[21]

Svitlana Volkova and Eric Bell . 2017. Identifying Effective Signals to Predict Deleted and Suspended Accounts on Twitter Across Languages. In AAAI International Conference on Weblogs and Social Media.

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Burruss GWeber CFisk NGiovannetti F(2025)Pandemic and predation: an analysis of economic stressors and online scam susceptibility during COVID-19Journal of Crime and Justice10.1080/0735648X.2025.2464015(1-16)Online publication date: 14-Feb-2025
https://doi.org/10.1080/0735648X.2025.2464015
Li NGao Y(2024)Modified fractional order social media addiction modeling and sliding mode control considering a professionally operating populationElectronic Research Archive10.3934/era.202418232:6(4043-4073)Online publication date: 2024
https://doi.org/10.3934/era.2024182
Pandey AAnanya BVerma VAgnihotri SJain UVijayakumar P(2024)Malicious Account Detection Using ANNData Science and Big Data Analytics10.1007/978-981-99-9179-2_11(135-148)Online publication date: 17-Mar-2024
https://doi.org/10.1007/978-981-99-9179-2_11
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Index Terms

Worth its Weight in Likes: Towards Detecting Fake Likes on Instagram
1. Information systems
  1. World Wide Web
    1. Web applications
      1. Social networks
2. Networks
  1. Network types
    1. Overlay and other logical network structures
      1. Online social networks

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Published In

cover image ACM Conferences

WebSci '18: Proceedings of the 10th ACM Conference on Web Science

May 2018

399 pages

ISBN:9781450355636

DOI:10.1145/3201064

General Chairs:
Hans Akkermans
Vrije Universiteit Amsterdam, The Netherlands
,
Kathy Fontaine
Rensselaer Polytechnic Institute, USA
,
Ivar Vermeulen
Vrije Universiteit Amsterdam, The Netherlands
,
Program Chairs:
Geert-Jan Houben
TU Delft, The Netherlands
,
Matthew S. Weber
Rutgers University, New Jersey, USA

Copyright © 2018 ACM.

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

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 May 2018

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WebSci '18

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SIGWEB

WebSci '18: 10th ACM Conference on Web Science

May 27 - 30, 2018

Amsterdam, Netherlands

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WebSci '18 Paper Acceptance Rate 30 of 113 submissions, 27%;

Overall Acceptance Rate 245 of 933 submissions, 26%

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Cited By

Burruss GWeber CFisk NGiovannetti F(2025)Pandemic and predation: an analysis of economic stressors and online scam susceptibility during COVID-19Journal of Crime and Justice10.1080/0735648X.2025.2464015(1-16)Online publication date: 14-Feb-2025
https://doi.org/10.1080/0735648X.2025.2464015
Li NGao Y(2024)Modified fractional order social media addiction modeling and sliding mode control considering a professionally operating populationElectronic Research Archive10.3934/era.202418232:6(4043-4073)Online publication date: 2024
https://doi.org/10.3934/era.2024182
Pandey AAnanya BVerma VAgnihotri SJain UVijayakumar P(2024)Malicious Account Detection Using ANNData Science and Big Data Analytics10.1007/978-981-99-9179-2_11(135-148)Online publication date: 17-Mar-2024
https://doi.org/10.1007/978-981-99-9179-2_11
Metzler HGarcia D(2023)Social Drivers and Algorithmic Mechanisms on Digital MediaPerspectives on Psychological Science10.1177/1745691623118505719:5(735-748)Online publication date: 19-Jul-2023
https://doi.org/10.1177/17456916231185057
Caruccio LCimino GCirillo SDesiato DPolese GTortora G(2023)Malicious Account Identification in Social Network PlatformsACM Transactions on Internet Technology10.1145/362509723:4(1-25)Online publication date: 17-Nov-2023
https://dl.acm.org/doi/10.1145/3625097
Shah MJoshi HGupta N(2023)Implementation of Social Media Fake Profile Identification Using A Novel Machine Learning Technique2023 IEEE International Conference on ICT in Business Industry & Government (ICTBIG)10.1109/ICTBIG59752.2023.10455989(1-9)Online publication date: 8-Dec-2023
https://doi.org/10.1109/ICTBIG59752.2023.10455989
Chan THung KTse D(2023)Comparing E-Commerce Micro- and Macroinfluencers in TikTok Videos: Effects of Strategies on Audience Likes, Audience Shares, and Brand SalesJournal of Interactive Advertising10.1080/15252019.2023.227325323:4(307-322)Online publication date: 27-Nov-2023
https://doi.org/10.1080/15252019.2023.2273253
Nevado-Catalán DPastrana SVallina-Rodriguez NTapiador J(2023)An analysis of fake social media engagement servicesComputers and Security10.1016/j.cose.2022.103013124:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.cose.2022.103013
Aljabri MZagrouba RShaahid AAlnasser FSaleh AAlomari D(2023)Machine learning-based social media bot detection: a comprehensive literature reviewSocial Network Analysis and Mining10.1007/s13278-022-01020-513:1Online publication date: 5-Jan-2023
https://doi.org/10.1007/s13278-022-01020-5
Kadam NSharma S(2023)Social Media Fake Profile Classification: A New Machine Learning ApproachAdvances in Data-driven Computing and Intelligent Systems10.1007/978-981-99-0981-0_62(823-839)Online publication date: 22-Jun-2023
https://doi.org/10.1007/978-981-99-0981-0_62
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