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.
Also related to our research is work done on the automatic identification of deceptive content, which has explored domains such as forums, consumer reviews websites, online advertising, online dating, and crowdfounding platforms @cite_5 @cite_9 @cite_12 @cite_4 @cite_1 . Linguistic clues such as self references or positive and negative words have been used to profile true tellers from liars @cite_13 . Other work has focused on analyzing the number of words, sentences, self references, affect, spatial and temporal information associated with deceptive content @cite_2 . Expressivity, informality, diversity and non-immediacy have also been explored to identify deceitful behaviors @cite_1 .
@cite_4 @cite_9 @cite_1 @cite_2 @cite_5 @cite_13 @cite_12
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The widespread use of deception in online sources has motivated the need for methods to automatically profile and identify deceivers. This work explores deception, gender and age detection in short texts using a machine learning approach. First, we collect a new open domain deception dataset also containing demographic data such as gender and age. Second, we extract feature sets including n-grams, shallow and deep syntactic features, semantic features, and syntactic complexity and readability metrics. Third, we build classifiers that aim to predict deception, gender, and age. Our findings show that while deception detection can be performed in short texts even in the absence of a predetermined domain, gender and age prediction in deceptive texts is a challenging task. We further explore the linguistic differences in deceptive content that relate to deceivers gender and age and find evidence that both age and gender play an important role in people’s word choices when fabricating lies. A growing field in computer applications is the use of algorithms to spot the lie. The most promising area within this field is the analysis of the language of the liar since speakers effectively control only the meaning they wish to convey, but not the linguistic style of the communication. With the advent of computational means to analyze language, we now have the ability to recognize differences in the way speakers phrase their lies as opposed to their truths. The main goal of this book is to cover the advances of the last 10 years in automatically discriminating truths from lies. To give the reader a grounding in deception studies, it describes a range of behaviors (physiological, gestural as well as verbal) that have been proposed as indicators of deception. An overview of the primary psychological and cognitive theories that have been offered as explanations of deceptive behaviors gives context for the description of specific behaviors. The book also addresses the differences between data collected in a laboratory and real-world data with respect to the emotional and cognitive state of the liar. It discusses sources of real-world data and problematic issues in its collection and identifies the primary areas in which applied studies based on real-world data are critical, including police, security, border crossing, customs, and asylum interviews; congressional hearings; financial reporting; legal depositions; human resource evaluation; predatory communications that include Internet scams, identity theft, fraud, and false product reviews. Having established the background, the book concentrates on computational analyses of deceptive verbal behavior, which have enabled the field of deception studies to move from individual cues to overall differences in behavior. The book concludes with a set of open questions that the computational work has generated. The present study investigates changes in both the sender's and the target's linguistic style across truthful and deceptive dyadic communication in a synchronous text-based setting. A computer-based analysis of 242 transcripts revealed that senders produced more words overall, decreased their use of self-oriented pronouns but increased other-oriented pronouns, and used more sense-based descriptions (e.g., seeing, touching) when lying than when telling the truth. In addition, motivated senders avoided causal terms during deception, while unmotivated senders relied more heavily on simple negations. Receivers used more words when being deceived, but they also asked more questions and used shorter sentences when being lied to than when being told the truth, especially when the sender was unmotivated. These findings are discussed in terms of their implications for linguistic style matching and interpersonal deception theory. Deception detection has been receiving an increasing amount of attention from the computational linguistics, speech, and multimodal processing communities. One of the major challenges encountered in this task is the availability of data, and most of the research work to date has been conducted on acted or artificially collected data. The generated deception models are thus lacking real-world evidence. In this paper, we explore the use of multimodal real-life data for the task of deception detection. We develop a new deception dataset consisting of videos from reallife scenarios, and build deception tools relying on verbal and nonverbal features. We achieve classification accuracies in the range of 77-82 when using a model that extracts and fuses features from the linguistic and visual modalities. We show that these results outperform the human capability of identifying deceit.