Paying for Likes? Understanding Facebook like fraud using honeypots

Facebook pages offer an easy way to reach out to a very large audience as they can easily be promoted using Facebook's advertising platform. Recently, the number of likes of a Facebook page has become a measure of its popularity and profitability, and an underground market of services boosting page likes, aka like farms, has emerged. Some reports have suggested that like farms use a network of profiles that also like other pages to elude fraud protection algorithms, however, to the best of our knowledge, there has been no systematic analysis of Facebook pages' promotion methods. This paper presents a comparative measurement study of page likes garnered via Facebook ads and by a few like farms. We deploy a set of honeypot pages, promote them using both methods, and analyze garnered likes based on likers' demographic, temporal, and social characteristics. We highlight a few interesting findings, including that some farms seem to be operated by bots and do not really try to hide the nature of their operations, while others follow a stealthier approach, mimicking regular users' behavior

The Effect of Hiding Dislikes on the Use of YouTube's Like and Dislike Features

Using data from a major international news organization, we investigate the effect of hiding the count of dislikes from YouTube viewers on the propensity to use the video like/dislike features. We compare one entire month of videos before (n = 478) and after (n = 394) YouTube began hiding the dislikes counts. Collectively, these videos had received 450,200 likes and 41,892 dislikes. To account for content variability, we analyze the likes/dislikes by sentiment class (positive, neutral, negative). Results of chi-square testing show that while both likes and dislikes decreased after the hiding, dislikes decreased substantially more. We repeat the analysis with four other YouTube news channels in various languages (Arabic, English, French, Spanish) and one non-news organization, with similar results in all but one case. Findings from these multiple organizations suggest that YouTube hiding the number of dislikes from viewers has altered the user-platform interactions for the like/dislike features. Therefore, comparing the like/dislike metrics before and after the removal would give invalid insights into users’ reactions to content on YouTube.© AuthorACM 2022. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in WebSci '22: 14th ACM Web Science Conference 2022, http://dx.doi.org/10.1145/3501247.3531546fi=vertaisarvioitu|en=peerReviewed

Characterizing Key Stakeholders in an Online Black-Hat Marketplace

Over the past few years, many black-hat marketplaces have emerged that facilitate access to reputation manipulation services such as fake Facebook likes, fraudulent search engine optimization (SEO), or bogus Amazon reviews. In order to deploy effective technical and legal countermeasures, it is important to understand how these black-hat marketplaces operate, shedding light on the services they offer, who is selling, who is buying, what are they buying, who is more successful, why are they successful, etc. Toward this goal, in this paper, we present a detailed micro-economic analysis of a popular online black-hat marketplace, namely, SEOClerks.com. As the site provides non-anonymized transaction information, we set to analyze selling and buying behavior of individual users, propose a strategy to identify key users, and study their tactics as compared to other (non-key) users. We find that key users: (1) are mostly located in Asian countries, (2) are focused more on selling black-hat SEO services, (3) tend to list more lower priced services, and (4) sometimes buy services from other sellers and then sell at higher prices. Finally, we discuss the implications of our analysis with respect to devising effective economic and legal intervention strategies against marketplace operators and key users.Comment: 12th IEEE/APWG Symposium on Electronic Crime Research (eCrime 2017

What Influences Influencers? Hiding Popularity Signals and Influencer Behavior

The burgeoning popularity of social media has shifted how social media users share and seek information through online platforms. Social media users are often motivated to show the “perfect side” of themselves on the platform, resulting in sharing manipulated appearances and positive aspects of their lives in order to garner more “likes” when comparing their popularity to others. Thus, social media users may often face inauthentic information, which may affect their behaviors on the platform. In this study, we utilize a change in Instagram policy—where they hide the number of likes from the platform— which started in September 2019 in East Asia. Specifically, we examine influencers’ post-generating behavior and post characteristics (e.g., whether it is focused on product vs influencers themselves and the degree of image manipulation). The results show that the number of endorsement postings increases, and influencers are more likely to generate influencer-focused postings after the intervention. In addition, we find that such effects are accentuated when influencers have a the larger follower base. Lastly, our findings suggest that the economic benefit (e.g., total weekly sales) that influencers gain increases after the intervention; however, such an effect is attenuated with influencers having a larger number of followers

When more likes is not better: the consequences of high and low likes-to-followers ratios for perceived account credibility and social media marketing effectiveness

Previous research on social media marketing assumes that the more followers or “likes” an individual or company has on social media, the better. The current research is the first that challenges this assumption by showing that people make inferences about the credibility of social media accounts based on the number of likes a post receives relative to the size of its likely audience. The findings indicate that high as well as low likes-to-followers ratios negatively influence the perceived credibility of the account and, as such, dampen social media marketing effectiveness. The addition of hashtags is identified as a way to guard against the negative impact of high likes-to-followers ratios. Managers, (aspiring) influencers, and people in general involved in (personal) branding on social media can use the present findings to maximize the effectiveness of their social media marketing strategy.This work was supported by the Spanish Ministry of Economics [grant number ECO2017-87369-

Uncovering Download Fraud Activities in Mobile App Markets

Download fraud is a prevalent threat in mobile App markets, where fraudsters manipulate the number of downloads of Apps via various cheating approaches. Purchased fake downloads can mislead recommendation and search algorithms and further lead to bad user experience in App markets. In this paper, we investigate download fraud problem based on a company's App Market, which is one of the most popular Android App markets. We release a honeypot App on the App Market and purchase fake downloads from fraudster agents to track fraud activities in the wild. Based on our interaction with the fraudsters, we categorize download fraud activities into three types according to their intentions: boosting front end downloads, optimizing App search ranking, and enhancing user acquisition&retention rate. For the download fraud aimed at optimizing App search ranking, we select, evaluate, and validate several features in identifying fake downloads based on billions of download data. To get a comprehensive understanding of download fraud, we further gather stances of App marketers, fraudster agencies, and market operators on download fraud. The followed analysis and suggestions shed light on the ways to mitigate download fraud in App markets and other social platforms. To the best of our knowledge, this is the first work that investigates the download fraud problem in mobile App markets.Comment: Published as a conference paper in IEEE/ACM ASONAM 201

I/O: Reinforcing Newsmaking Practices Through Algorithmic Media

Recent developments in communication and information technology have disrupted the long-established dominance of mass media over the production and distribution of news. As an effort to reclaim their role of society’s information gatekeeper, media companies absorb digital technology as instruments of institutional power to reproduce its own logic in the digital space. This paper dis-cusses two interrelated modalities of algorithmic news: economically efficient production, where news outlets utilize quantitative metrics to improve content effectiveness and desirability; and shared-gatekeeping, where visibility and distribution of information are contextual and based on users’ behaviour. The paper proposes that algorithmic media hides under its supposed objectivity and neutrality to become a new gatekeeper “organism”, which not only regulates flows of infor-mation, but also interprets and negotiates both public interests and the value of the news