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Social media has become a powerful and efficient platform for information diffusion. The increasing pervasiveness of social media use, however, has brought about the problems of fraudulent accounts that are intended to diffuse misinformation or malicious contents. Twitter recently released comprehensive archives of fraudulent tweets that are possibly connected to a propaganda effort of Internet Research Agency (IRA) on the 2016 U.S. presidential election. To understand information diffusion in fraudulent networks, we analyze structural properties of the IRA retweet network, and develop deep neural network models to detect fraudulent tweets. The structure analysis reveals key characteristics of the fraudulent network. The experiment results demonstrate the superior performance of the deep learning technique to a traditional classification method in detecting fraudulent tweets. The findings have potential implications for curbing online misinformation. 

Traditional citation analysis methods have been criticized because their theoretical base of statistical counts does not reflect the motive or judgment of citing authors. In particular, self-citations may give undue credits to a cited article or mislead scientific development. This research aims to answer the question of whether self-citation is biased by probing into the motives and context of citations. It takes an integrated and fine-grained view of self-citations by examining them via multiple lenses—polarity, density, and location of citations. In addition, it explores potential moderating effects of citation level and associations among location contexts of citations to the same references for the first time. We analyzed academic publications across different topics and disciplines using both qualitative and quantitative methods. The results provide evidence that self-citations are free of bias in terms of citation density and polarity uncertainty, but they can be biased with respect to positivity and negativity of citations. Furthermore, this study reveals impacts of self-citing behavior on some citation patterns involving citation density, location concentration, and associations. The examination of self-citing behavior from those new perspectives shed new lights on the nature and function of self-citing behavior. 

Online consumer reviews contain rich yet implicit information regarding consumers’ preferences for specific aspects of products/services. Extracting aspects from online consumer reviews has been recognized as a valuable step in performing fine-grained analytical tasks (e.g. aspect-based sentiment analysis). Extant approaches to aspect extraction are dominated by discrete models. Despite explosive research interests in continuous-space language models in recent years, these models have yet to be explored for the task of extracting product/service aspects from online consumer reviews. In addition, previous continuous-space models for information extraction have largely overlooked the role of semantic information embedded in texts. In this study, we propose an approach of aspect extraction that leverages semantic information from WordNet in conjunction of building continuous-space language models from review texts. The experiment results with online restaurant reviews demonstrate that the WordNet-guided continuous-space language models outperform both discrete models and continuous-space language models without incorporating the semantic information. The research findings have important implications for understanding consumer preferences and improving business performances. 

Craigslist is a popular online customer-to-customer marketplace, which has attracted millions of consumers for trading and purchasing secondhand items. Because of the high financial return that sellers could gain from using this site and the anonymity option that the website provides to its users, Craigslist is highly subject to fraudulent activities. The primary objective of this study is to detect scam ads on Craigslist. Based on the related literature and our observations of ads collected from the platform, we develop a heuristic method for identifying scam ads. We evaluate the proposed heuristics by conducting an experiment and performing additional data analyses using real data. The results provide preliminary evidence for efficacy of the heuristics developed in this study. 

Despite the tremendous role of online consumer reviews (OCRs) in facilitating consumer purchase decision making, the potential inconsistency between product ratings and review content could cause the uncertainty and confusions of prospect consumers toward a product. This research is aimed to investigate such inconsistency so as to better assist potential consumers with making purchase decisions. First, this study extracted a reviewer’s sentiments from review text via sentiment analysis. Then, it examined the correlation and inconsistency between product ratings and review sentiments via Pearson correlation coefficients (PCC) and box plots. Next, we compared such inconsistency patterns between fake and authentic reviews. Based on an analysis of 24,539 Yelp reviews, we find that although the ratings and sentiments are highly correlated, the inconsistency between the two is more salient in fake reviews than in authentic reviews. The comparison also reveals different inconsistency patterns between the two types of reviews.