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Detection of Fraudulent Tweets: An Empirical Investigation Using Network Analysis and Deep Learning TechniqueSocial 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.more »
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 samemore »
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, wemore »
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 datamore »
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.more »