Measuring, Characterizing, and Detecting Facebook Like Farms

Social networks offer convenient ways to seamlessly reach out to large audiences. In particular, Facebook pages are increasingly used by businesses, brands, and organizations to connect with multitudes of users worldwide. As the number of likes of a page has become a de-facto measure of its popularity and profitability, an underground market of services artificially inflating page likes, aka like farms, has emerged alongside Facebook's official targeted advertising platform. Nonetheless, there is little work that systematically analyzes Facebook pages' promotion methods. Aiming to fill this gap, we present a honeypot-based comparative measurement study of page likes garnered via Facebook advertising and from popular like farms. First, we analyze likes based on demographic, temporal, and social characteristics, and find 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. Next, we look at fraud detection algorithms currently deployed by Facebook and show that they do not work well to detect stealthy farms which spread likes over longer timespans and like popular pages to mimic regular users. To overcome their limitations, we investigate the feasibility of timeline-based detection of like farm accounts, focusing on characterizing content generated by Facebook accounts on their timelines as an indicator of genuine versus fake social activity. We analyze a range of features, grouped into two main categories: lexical and non-lexical. We find that like farm accounts tend to re-share content, use fewer words and poorer vocabulary, and more often generate duplicate comments and likes compared to normal users. Using relevant lexical and non-lexical features, we build a classifier to detect like farms accounts that achieves precision higher than 99% and 93% recall.Comment: To appear in ACM Transactions on Privacy and Security (TOPS

Who’s holding the bag? Accountability in the criminal justice system

Lack of accountability and transparency are major impediments in efforts to minimize delays, ensure due process of law and reduce backlogged cases in the criminal justice system of . Existing oversight mechanisms to track cases through physical files and archives are prone to tampering and damage. The problem is particularly acute since there is little or no coordination between police, prosecution, and courts. There is no meaningful consolidation of crime and prosecution analytics and a total absence of transparency in the process. The current system makes it difficult to see who’s holding the proverbial bag. _x000D_ This paper presents results from a first of its-kind survey of our criminal justice system in . We highlight the importance and policy implications of our work by presenting empirical data from 750 prosecution vouchers using the results to motivate a case-flow design that integrates and maps the case-management practices of all three institutions involved

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

CanaryTrap: Detecting Data Misuse by Third-Party Apps on Online Social Networks

Online social networks support a vibrant ecosystem of third-party apps that get access to personal information of a large number of users. Despite several recent high-profile incidents, methods to systematically detect data misuse by third-party apps on online social networks are lacking. We propose CanaryTrap to detect misuse of data shared with third-party apps. CanaryTrap associates a honeytoken to a user account and then monitors its unrecognized use via different channels after sharing it with the third-party app. We design and implement CanaryTrap to investigate misuse of data shared with third-party apps on Facebook. Specifically, we share the email address associated with a Facebook account as a honeytoken by installing a third-party app. We then monitor the received emails and use Facebook’s ad transparency tool to detect any unrecognized use of the shared honeytoken. Our deployment of CanaryTrap to monitor 1,024 Facebook apps has uncovered multiple cases of misuse of data shared with third-party apps on Facebook including ransomware, spam, and targeted advertising

A Classification Based Framework to Predict Viral Threads

Online social media allows consumers to engage with each other and to create, share, discuss and modify user-generated content in a highly interactive way. Social media platforms have therefore become critical for companies trying to gauge the pulse of consumers, help identify issues faster, receive immediate feedback on products and offering etc. An effective social media strategy therefore requires companies to mine large volumes of structured unstructured and semi-structured online textual data in order to gain insights into the underlying traits of the consumers and prevailing public opinion. These insights can provide opportunities for market research, protection of brand reputation and a mechanism to gauge user preferences in an attempt to maximize customer satisfaction and consumer-brand engagement. In this paper, we propose and evaluate a classification based framework to predict thread lengths in online discussion forums in order to identify potential topics that may of interest to a particular online community. We identify and evaluate several key features of viral social media conversations through extensive experiments conducted on health 2.0 datasets. We also present a pharmaceutical industry based case study to illustrate how well the viral thread topics relate to real world events