The proliferation of misleading information in everyday access media outlets such as social media feeds, news blogs, and online newspapers have made it challenging to identify trustworthy news sources, thus increasing the need for computational tools able to provide insights into the reliability of online content. In this paper, we focus on the automatic identification of fake content in online news. Our contribution is twofold. First, we introduce two novel datasets for the task of fake news detection, covering seven different news domains. We describe the collection, annotation, and validation process in detail and present several exploratory analysis on the identification of linguistic differences in fake and legitimate news content. Second, we conduct a set of learning experiments to build accurate fake news detectors. In addition, we provide comparative analyses of the automatic and manual identification of fake news.
  Crowdfunding sites with recent explosive growth are equally attractive platforms for swindlers or scammers. Though the growing number of articles on crowdfunding scams indicate that the fraud threats are accelerating, there has been little knowledge on the scamming practices and patterns. The key contribution of this research is to discover the hidden clues in the text by exploring linguistic features to distinguish scam campaigns from non-scams. Our results indicate that by providing less information and writing more carefully (and less informally), scammers deliberately try to deceive people; (i) they use less number of words, verbs, and sentences in their campaign pages. (ii) scammers make less typographical errors, 4.5-4.7 times lower than non-scammers.(iii) Expressivity of scams is 2.6-8.5 times lower as well. Modality is an important context factor in deception, which is context-dependent. In order to build a reliable and flexible tool for automatic-deception-detection (ADD), we investigated the characteristics of verbal cues to deceptive behavior in three modalities: text, audio and face-to-face communication. Seven categories of verbal cues (21 cues) were studied: quantity, complexity, diversity, verb nonimmediacy, uncertainty, specificity and affect. After testing the interaction effects between modality and condition (deception or truth), we found significance only with specificity and observed that differences between deception and truth were in general consistent across the three modalities. However, modality had strong effects on verbal cues. For example, messages delivered face-to-face were largest in quantity (number of words, verbs, and sentences), followed by the audio modality. Text had the sparsest examples. These modality effects are an important factor in building baselines in ADD tools, because they make it possible to use them to adjust the baseline for an unknown modality according to a known baseline, thereby simplifying the process of ADD. The paper discusses in detail the implications of these findings on modality effects in three modalities. This article explores the operation of warrants, connections between online and real-world identities, on deceptive behavior in computer-mediated communication. A survey of 132 participants assessed three types of warrants (the use of a real name, a photo, and the presence of real-world acquaintances) in five different media: IM, Forums, Chat, Social Networking Sites (SNS) and Email. The effect of warrants on lies about demographic information (e.g., age, gender, education, etc.), one's interests (e.g., religion, music preferences, etc.), and the seriousness of lies was assessed. Overall, deception was observed most frequently in Chat and least often in SNS and Email. The relationship between warrants and deception was negative and linear, with warrants suppressing the frequency and seriousness of deception regardless of medium, although real-world acquaintances were especially powerful in constraining deception in SNS and emails. Telling lies often requires creating a story about an experience or attitude that does not exist. As a result, false stories may be qualitatively different from true stories. The current project investigated the features of linguistic style that distinguish between true and false stories. In an analysis of five independent samples, a computer-based text analysis program correctly classified liars and truth-tellers at a rate of 67 when the topic was constant and a rate of 61 overall. Compared to truth-tellers, liars showed lower cognitive complexity, used fewer self-references and other-references, and used more negative emotion words. With the rapid growth of the Internet, online advertisement plays a more and more important role in the advertising market. One of the current and widely used revenue models for online advertising involves charging for each click based on the popularity of keywords and the number of competing advertisers. This pay-per-click model leaves room for individuals or rival companies to generate false clicks (i.e., click fraud), which pose serious problems to the development of healthy online advertising market. To detect click fraud, an important issue is to detect duplicate clicks over decaying window models, such as jumping windows and sliding windows. Decaying window models can be very helpful in defining and determining click fraud. However, although there are available algorithms to detect duplicates, there is still a lack of practical and effective solutions to detect click fraud in pay-per-click streams over decaying window models. In this paper, we address the problem of detecting duplicate clicks in pay-per-click streams over jumping windows and sliding windows, and are the first that propose two innovative algorithms that make only one pass over click streams and require significantly less memory space and operations. GBF algorithm is built on group Bloom filters which can process click streams over jumping windows with small number of sub-windows, while TBF algorithm is based on a new data structure called timing Bloom filter that detects click fraud over sliding windows and jumping windows with large number of sub-windows. Both GBF algorithm and TBF algorithm have zero false negative. Furthermore, both theoretical analysis and experimental results show that our algorithms can achieve low false positive rate when detecting duplicate clicks in pay-per-click streams over jumping windows and sliding windows.