Detection & Analysis of Follow Back Accounts on Social Media

Core Concepts

Follow back accounts manipulate social media platforms by inflating follower counts, potentially causing harm through misinformation. This study provides insights into their characteristics and behaviors.

Abstract

Abstract: First large-scale analysis of follow back accounts. Identified 12 communities from 12 countries. Proposed a classifier for detecting follow back accounts. Introduction: Popular social media users employ growth strategies. Follow back accounts manipulate follower counts. Summary of Findings: Discovered 2759 follow back accounts and 12 communities. Proposed a classifier with moderate success in classification. Related Work: Extensive research on manipulation strategies in social media. Definition: Defined follow back behavior as reciprocal followings to inflate follower counts. Data Collection: Used honeypot approach to collect ground truth data on follow back accounts. Communities of Follow Back Accounts: Identified characteristics and behaviors of different communities based on country or interest. Characterization: Analyzed differences between follow back and other accounts in terms of activity, engagement, reciprocity, etc. Platform Abuse: Examined coordination, automation, trains conduct/ride behavior among communities. Suspensions: Low suspension rate for follow back accounts despite T.O.S. violations.

Stats

"We discovered and describe 12 communities of follow" "We found that a tabular data classifier using features created from profile metadata" "Our initial ground truth dataset consists of"

Quotes

"We propose a classifier for such accounts and report that models employing profile metadata and the ego network demonstrate promising results." "Despite their potential harm, such accounts are understudied. We fill this gap and present the first large-scale study of follow back accounts."

Key Insights Distilled From

#TeamFollowBack

by Tuğr... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.15856.pdf

Deeper Inquiries

How can social media platforms improve the detection and handling of follow-back accounts?

Social media platforms can enhance the detection and management of follow-back accounts by implementing more robust algorithms that can identify patterns indicative of such behavior. This could involve analyzing account activity, engagement ratios, follower-to-following ratios, response times to follows, and other metrics to flag suspicious accounts. Additionally, incorporating machine learning models trained on labeled datasets like the one in this study could help automate the process of identifying follow-back accounts at scale. Platforms should also regularly update their terms of service to explicitly prohibit follow-back schemes and promptly suspend or remove violating accounts.

What ethical considerations should be taken into account when studying or classifying such accounts?

When studying or classifying follow-back accounts, several ethical considerations must be addressed. Firstly, researchers must ensure that they are not infringing on users' privacy rights by collecting data without consent or disclosing sensitive information about individuals. It is crucial to maintain transparency throughout the research process and obtain necessary approvals if dealing with human subjects. Moreover, researchers should avoid contributing to any potential harm caused by these accounts by responsibly reporting findings without amplifying misinformation spread by them.

How might the findings of this study impact the future landscape of social media interactions?

The findings from this study shed light on a previously understudied aspect of social media behavior - follow-back accounts - which have implications for platform integrity and user trust. By understanding the characteristics and behaviors associated with these accounts, social media platforms can develop better strategies for detecting and mitigating their influence. This knowledge may lead to improved algorithms for identifying fake engagement tactics used by malicious actors, ultimately creating a more authentic online environment where genuine interactions are valued over artificial popularity metrics.