Auditing is critical to ensuring the fairness and reliability of decision-making systems. However, auditing a black-box system for bias can be challenging due to the lack of transparency in the model’s internal workings. In many web applications, such as Yelp, it is challenging, if not impossible, to manipulate their inputs systematically to identify bias in the output. Yelp connects users and businesses, where users identify new businesses and simultaneously express their experiences through reviews. Yelp recommendation software moderates user-provided content by categorizing it into recommended and not-recommended sections. The recommended reviews, among other attributes, are used by Yelp’s ranking algorithm to rank businesses in a neighborhood. Due to Yelp’s substantial popularity and its high impact on local businesses’ success, understanding the bias of its algorithms is crucial. This data-driven study, for the first time, investigates the bias of Yelp’s business ranking and review recommendation system. We examine three hypotheses to assess if Yelp’s recommendation software shows bias against reviews of less established users with fewer friends and reviews and if Yelp’s business ranking algorithm shows bias against restaurants located in specific neighborhoods, particularly in hotspot regions, with specific demographic compositions. Our findings show that reviews of less-established users are disproportionately categorized as not-recommended. We also find a positive association between restaurants’ location in hotspot regions and their average exposure. Furthermore, we observed some cases of severe disparity bias in cities where the hotspots are in neighborhoods with less demographic diversity or higher affluence and education levels.

Social media platforms employ various content moderation techniques to remove harmful, offensive, and toxic content, with moderation levels varying across platforms and evolving over time. Parler, a fringe platform popular among conservative users, initially had minimal moderation, promoting itself as a space for open discussion. However, in 2021, it was removed from the Apple and Google App Stores and suspended from Amazon Web Services due to inadequate moderation of harmful content. After a month-long suspension, Parler returned with stricter guidelines, offering a unique opportunity to study the impact of platform-wide policy changes on user behavior and content outcomes. In this paper, we analyzed Parler data to assess the causal associations of these moderation changes on content toxicity and factuality. Using a longitudinal dataset of 17M posts from 432K users, who were active both before and after replatforming, we employed quasi-experimental analysis, controlling for confounding factors. We introduced a novel approach by using data from another social media platform, Twitter, to account for a critical confounding factor: offline events. This allowed us to isolate the effects of Parler's replatforming policies from external real-world influences. Our findings demonstrate that Parler's moderation changes are causally associated with a significant reduction in all forms of toxicity (p < 0.001). Additionally, we observed an increase in the factuality of the news sites shared and a reduction in the number of conspiracy/ pseudoscience sources.

Abortion remains one of the most controversial topics, especially after overturning Roe v. Wade ruling in the United States. Previous literature showed that the illegality of abortion could have serious consequences, as women might seek unsafe pregnancy terminations leading to increased maternal mortality rates and negative effects on their reproductive health. Therefore, the stances of the abortion-related Facebook posts were analyzed at the state level in the United States from May 4 until June 30, 2022, right after the Supreme Court’s decision was disclosed. In more detail, a pre-trained Transformer architecture-based model was fine-tuned on a manually labeled training set to obtain a stance detection model suitable for the collected dataset. Afterward, we employed appropriate statistical tests to examine the relationships between public opinion regarding abortion, abortion legality, political leaning, and factors measuring the overall population’s health, health knowledge, and vulnerability per state. We found that infant mortality rate, political affiliation, abortion rates, and abortion legality are associated with stances toward abortion at the state level in the US. While aligned with existing literature, these findings indicate how public opinion, laws, and women’s and infants’ health are related, as well as how these relationships can be demonstrated by using social media data.

Prior works have shown connections between online toxicity attacks, such as harassment, cyberbullying, and hate speech, and the subsequent increase in offline violence, as well as negative psychological effects on victims. These correlations are primarily identified through user studies conducted via virtual environments, simulations, and questionnaires. However, no work has investigated how, in practice and authentically, people react to online toxicity both emotionally, showing anger, anxiety, and sadness, and behaviorally in terms of engaging with and responding to toxicity instigators, considering conversations as a whole and the relation between emotions and behaviors. This data-driven study investigates the effect of toxicity on Twitter users' behaviors and emotions considering confounding factors, such as account identifiability, activity, and conversation's structure and topic. We collected about 80K Twitter conversations and identified those with and without toxic replies. Performing statistical tests along with propensity score matching, we investigated the causal association of receiving toxicity and users' responses. We found that authors of conversations with toxic replies are more likely to engage in conversations, reply in a toxic way, and unfollow toxicity instigators. In terms of users' emotional responses, we found that sadness and anger after the first toxic reply are more likely to increase as the amount of toxicity increases. These findings not only emphasize the negative emotional and behavioral effects of online toxicity on social media users but also, as demonstrated in this paper, can be utilized to build prediction models for users' reactions, which could then aid the implementation of proactive detection and intervention measures helping users in such situations.

The rapid growth in popularity and hype surrounding digital assets such as art, video, and music in the form of non-fungible tokens (NFTs) has made them a lucrative investment opportunity, with NFT-based sales surpassing $25B in 2021 alone. However, the volatility and general lack of technical understanding of the NFT ecosystem have led to the spread of various scams. The success of an NFT heavily depends on its online virality. As a result, creators use dedicated promotion services to drive engagement to their projects on social media websites, such as Twitter. However, these services are also utilized by scammers to promote fraudulent projects that attempt to steal users' cryptocurrency assets, thus posing a major threat to the ecosystem of NFT sales. In this paper, we conduct a longitudinal study of 439 promotion services (accounts) on Twitter that have collectively promoted 823 unique NFT projects through giveaway competitions over a period of two months. Our findings reveal that more than 36% of these projects were fraudulent, comprising of phishing, rug pull, and pre-mint scams. We also found that a majority of accounts engaging with these promotions (including those for fraudulent NFT projects) are bots that artificially inflate the popularity of the fraudulent NFT collections by increasing their likes, followers, and retweet counts. This manipulation results in significant engagement from real users, who then invest in these scams. We also identify several shortcomings in existing anti-scam measures, such as blocklists, browser protection tools, and domain hosting services, in detecting NFT-based scams. We utilize our findings to develop and open-source a machine learning classifier tool that was able to proactively detect 382 new fraudulent NFT projects on Twitter.

Prior work has extensively studied misinformation related to news, politics, and health, however, misinformation can also be about technological topics. While less controversial, such misinformation can severely impact companies’ reputations and revenues, and users’ online experiences. Recently, social media has also been increasingly used as a novel source of knowledgebase for extracting timely and relevant security threats, which are fed to the threat intelligence systems for better performance. However, with possible campaigns spreading false security threats, these systems can become vulnerable to poisoning attacks. In this work, we proposed novel approaches for detecting misinformation about cybersecurity and privacy threats on social media, focusing on two topics with different types of misinformation: phishing websites and Zoom’s security & privacy threats. We developed a framework for detecting inaccurate phishing claims on Twitter. Using this framework, we could label about 9% of URLs and 22% of phishing reports as misinformation. We also proposed another framework for detecting misinformation related to Zoom’s security and privacy threats on multiple platforms. Our classifiers showed great performance with more than 98% accuracy. Employing these classifiers on the posts from Facebook, Instagram, Reddit, and Twitter, we found respectively that about 18%, 3%, 4%, and 3% of posts were misinformation. In addition, we studied the characteristics of misinformation posts, their authors, and their timelines, which helped us identify campaigns.

While online review services provide a two-way conversation between brands and consumers, malicious actors, including misbehaving businesses, have an equal opportunity to distort the reviews for their own gains. We propose OneReview, a method for locating fraudulent reviews, correlating data from multiple crowd-sourced review sites. Our approach utilizes Change Point Analysis to locate points at which a business' reputation shifts. Inconsistent trends in reviews of the same businesses across multiple websites are used to identify suspicious reviews. We then extract an extensive set of textual and contextual features from these suspicious reviews and employ supervised machine learning to detect fraudulent reviews.