Does "Like" Really Mean Like? A Study of the Facebook Fake Like Phenomenon and an Efficient Countermeasure

TL;DR

This paper investigates the vulnerability of Facebook's Like system to fake Likes, demonstrates how to generate fake Likes efficiently, and proposes a clustering-based detection method validated on thousands of websites.

Contribution

It introduces a proof-of-concept for fake Like generation, analyzes existing counterfeiting techniques, and develops an effective detection algorithm based on Like count pattern clustering.

Findings

Successfully generated 100 fake Likes in 5 minutes with a single account

Detected 16 suspicious fake Like buyers using the proposed method

Collected and analyzed Like count data from over 9,000 websites

Abstract

Social networks help to bond people who share similar interests all over the world. As a complement, the Facebook "Like" button is an efficient tool that bonds people with the online information. People click on the "Like" button to express their fondness of a particular piece of information and in turn tend to visit webpages with high "Like" count. The important fact of the Like count is that it reflects the number of actual users who "liked" this information. However, according to our study, one can easily exploit the defects of the "Like" button to counterfeit a high "Like" count. We provide a proof-of-concept implementation of these exploits, and manage to generate 100 fake Likes in 5 minutes with a single account. We also reveal existing counterfeiting techniques used by some online sellers to achieve unfair advantage for promoting their products. To address this fake Like problem, we study the varying patterns of Like count and propose an innovative fake Like detection method based on clustering. To evaluate the effectiveness of our algorithm, we collect the Like count history of more than 9,000 websites. Our experiments successfully uncover 16 suspicious fake Like buyers that show abnormal Like count increase patterns.