This research surveys the current state-of-the-art technologies that are instrumental in the adoption and development of fake news detection. "Fake news detection" is defined as the task of categorizing news along a continuum of veracity, with an associated measure of certainty. Veracity is compromised by the occurrence of intentional deceptions. The nature of online news publication has changed, such that traditional fact checking and vetting from potential deception is impossible against the flood arising from content generators, as well as various formats and genres.The paper provides a typology of several varieties of veracity assessment methods emerging from two major categories -linguistic cue approaches (with machine learning), and network analysis approaches. We see promise in an innovative hybrid approach that combines linguistic cue and machine learning, with network-based behavioral data. Although designing a fake news detector is not a straightforward problem, we propose operational guidelines for a feasible fake news detecting system.

Satire is an attractive subject in deception detection research: it is a type of deception that intentionally incorporates cues revealing its own deceptiveness. Whereas other types of fabrications aim to instill a false sense of truth in the reader, a successful satirical hoax must eventually be exposed as a jest. This paper provides a conceptual overview of satire and humor, elaborating and illustrating the unique features of satirical news, which mimics the format and style of journalistic reporting. Satirical news stories were carefully matched and examined in contrast with their legitimate news counterparts in 12 contemporary news topics in 4 domains (civics, science, business, and "soft" news). Building on previous work in satire detection, we proposed an SVMbased algorithm, enriched with 5 predictive features (Absurdity, Humor, Grammar, Negative Affect, and Punctuation) and tested their combinations on 360 news articles. Our best predicting feature combination (Absurdity, Grammar and Punctuation) detects satirical news with a 90% precision and 84% recall (F-score=87%). Our work in algorithmically identifying satirical news pieces can aid in minimizing the potential deceptive impact of satire.

A fake news detection system aims to assist users in detecting and filtering out varieties of potentially deceptive news. The prediction of the chances that a particular news item is intentionally deceptive is based on the analysis of previously seen truthful and deceptive news. A scarcity of deceptive news, available as corpora for predictive modeling, is a major stumbling block in this field of natural language processing (NLP) and deception detection. This paper discusses three types of fake news, each in contrast to genuine serious reporting, and weighs their pros and cons as a corpus for text analytics and predictive modeling. Filtering, vetting, and verifying online information continues to be essential in library and information science (LIS), as the lines between traditional news and online information are blurring.

Widespread adoption of internet technologies has changed the way that news is created and consumed. The current online news environment is one that incentivizes speed and spectacle in reporting, at the cost of fact-checking and verification. The line between user generated content and traditional news has also become increasingly blurred. This poster reviews some of the professional and cultural issues surrounding online news and argues for a two-pronged approach inspired by Hemingway's "automatic crap detector" (Manning, 1965) in order to address these problems: a) proactive public engagement by educators, librarians, and information specialists to promote digital literacy practices; b) the development of automated tools and technologies to assist journalists in vetting, verifying, and fact-checking, and to assist news readers by filtering and flagging dubious information.

This paper argues that big data can possess different characteristics, which affect its quality. Depending on its origin, data processing technologies, and methodologies used for data collection and scientific discoveries, big data can have biases, ambiguities, and inaccuracies which need to be identified and accounted for to reduce inference errors and improve the accuracy of generated insights. Big data veracity is now being recognized as a necessary property for its utilization, complementing the three previously established quality dimensions (volume, variety, and velocity), But there has been little discussion of the concept of veracity thus far. This paper provides a roadmap for theoretical and empirical definitions of veracity along with its practical implications. We explore veracity across three main dimensions: 1) objectivity/subjectivity, 2) truthfulness/deception, 3) credibility/implausibility -and propose to operationalize each of these dimensions with either existing computational tools or potential ones, relevant particularly to textual data analytics. We combine the measures of veracity dimensions into one composite index -the big data veracity index. This newly developed veracity index provides a useful way of assessing systematic variations in big data quality across datasets with textual information. The paper contributes to the big data research by categorizing the range of existing tools to measure the suggested dimensions, and to Library and Information Science (LIS) by proposing to account for heterogeneity of diverse big data, and to identify information quality dimensions important for each big data type.