• Corpus ID: 14728470

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

  title={Does "Like" Really Mean Like? A Study of the Facebook Fake Like Phenomenon and an Efficient Countermeasure},
  author={Xinye Lin and Mingyuan Xia and Xue Liu},
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… 
Likes are not Likes A Crowdworking Platform Analysis
Many Internet users rely on Online Social Networks (OSNs) in their daily lives to read news, find local restaurant recommendations, or learn about products. But lately OSNs have come under scrutiny
Examining the Demand for Spam: Who Clicks?
It is found that the volume of spam and clicking norms in a users' network are significantly related to individual consumption behavior, and that more active users are less likely to click, suggesting that experience and internet skill may create more savvy consumers.
Manipulating Visibility of Political and Apolitical Threads on Reddit via Score Boosting
This paper measures the effect of vote manipulation on article visibility and user engagement on Reddit by comparing sets of threads on Reddit whose visibility is artificially increased, and shows that vote manipulation has a significant impact on the visibility of the threads on both subreddits.
Discovering hidden suspicious accounts in online social networks
The forwarding message tree is introduced, which combines accounts based on the relations among their forwarded messages, and is proved to detect and delete hidden suspicious accounts with significant results.
Estimating Creditworthiness using Uncertain Online Data
The rules for credit lenders have become stricter since the financial crisis of 2007-2008. As a consequence, it has become more difficult for companies to obtain a loan. Many people and companies
Social network sites and requirements engineering: A systematic literature review
It is indicated that social network sites can be a major source that can be used successfully to extract and identify user requirements as well as challenges, unresolved issues, and future research directions.


Aiding the Detection of Fake Accounts in Large Scale Social Online Services
A new tool in the hands of OSN operators, which relies on social graph properties to rank users according to their perceived likelihood of being fake (SybilRank), which is computationally efficient and can scale to graphs with hundreds of millions of nodes, as demonstrated by the Hadoop prototype.
Facebook Tracks and Traces Everyone: Like This!
Numerous websites have implemented the Facebook Like button to let Facebook members share their interests, therewith promoting websites or news items. It is, thus, an important business tool for
We Are All Connected to Facebook ... by Facebook!
Privacy issues arising from thirdparty cookie use and connectivity of web activity and devices will be discussed, using the technical process behind the Facebook Like button as an example.
Detecting spammers on social networks
The results show that it is possible to automatically identify the accounts used by spammers, and the analysis was used for take-down efforts in a real-world social network.
The socialbot network: when bots socialize for fame and money
This paper adopts a traditional web-based botnet design and built a Socialbot Network (SbN): a group of adaptive socialbots that are orchestrated in a command-and-control fashion that is evaluated how vulnerable OSNs are to a large-scale infiltration by socialbots.
Understanding what they do with what they know
This work analyzes the ads shown to users during controlled browsing as well as examine the inferred demographics and interests shown in Ad Preference Managers provided by advertisers to understand what "they" (Web advertisers) actually do with the information available to them.
Detecting and characterizing social spam campaigns
This paper presents an initial study to quantify and characterize spam campaigns launched using accounts on online social networks, and analyzes a large anonymized dataset of asynchronous "wall" messages between Facebook users to detect and characterize coordinated spam campaigns.
Detecting Spammers on Twitter
With millions of users tweeting around the world, real time search systems and dierent types of mining tools are emerging to allow people tracking the repercussion of events and news on Twitter.
Uncovering social spammers: social honeypots + machine learning
It is found that the deployed social honeypots identify social spammers with low false positive rates and that the harvested spam data contains signals that are strongly correlated with observable profile features (e.g., content, friend information, posting patterns, etc.).
Don't follow me: Spam detection in Twitter
  • A. Wang
  • Computer Science
    2010 International Conference on Security and Cryptography (SECRYPT)
  • 2010
A spam detection prototype system to identify suspicious users on Twitter and a directed social graph model is proposed to explore the “follower” and “friend” relationships among Twitter to facilitate spam detection.