Victoria L. Rubin

Certainty Identification in Texts: Categorization Model and Manual Tagging Results

This chapter presents a theoretical framework and preliminary results for manual categorization of explicit certainty information in 32 English newspaper articles. Our contribution is in a proposed categorization model and analytical framework for certainty identification. Certainty is presented as a type of subjective information available in texts. Statements with explicit certainty markers were identified and categorized according to four hypothesized dimensions — level, perspective, focus, and time of certainty. The preliminary results reveal an overall promising picture of the presence of certainty information in texts, and establish its susceptibility to manual identification within the proposed four-dimensional certainty categorization analytical framework. Our findings are that the editorial sample group had a significantly higher frequency of markers per sentence than did the sample group of the news stories. For editorials, high level of certainty, writer’s point of view, and future and present time were the most populated categories. For news stories, the most common categories were high and moderate levels, directly involved third party’s point of view, and past time. These patterns have positive practical implications for automation.

Automatic deception detection: methods for finding fake news

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.

Deception detection for news: three types of fakes

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.

Misleading Online Content: Recognizing Clickbait as "False News"

Tabloid journalism is often criticized for its propensity for exaggeration, sensationalization, scare-mongering, and otherwise producing misleading and low quality news. As the news has moved online, a new form of tabloidization has emerged: ?clickbaiting.? ?Clickbait? refers to ?content whose main purpose is to attract attention and encourage visitors to click on a link to a particular web page? [?clickbait,? n.d.] and has been implicated in the rapid spread of rumor and misinformation online. This paper examines potential methods for the automatic detection of clickbait as a form of deception. Methods for recognizing both textual and non-textual clickbaiting cues are surveyed, leading to the suggestion that a hybrid approach may yield best results.

Epistemic modality: From uncertainty to certainty in the context of information seeking as interactions with texts

This article introduces a type of uncertainty that resides in textual information and requires epistemic interpretation on the information seekers part. Epistemic modality, as defined in linguistics and natural language processing, is a writers estimation of the validity of propositional content in texts. It is an evaluation of chances that a certain hypothetical state of affairs is true, e.g., definitely true or possibly true. This research shifts attention from the uncertainty-certainty dichotomy to a gradient epistemic continuum of absolute, high, moderate, low certainty, and uncertainty. An analysis of a New York Times dataset showed that epistemically modalized statements are pervasive in news discourse and they occur at a significantly higher rate in editorials than in news reports. Four independent annotators were able to recognize a gradation on the continuum but individual perceptions of the boundaries between levels were highly subjective. Stricter annotation instructions and longer coder training improved intercoder agreement results. This paper offers an interdisciplinary bridge between research in linguistics, natural language processing, and information seeking with potential benefits to design and implementation of information systems for situations where large amounts of textual information are screened manually on a regular basis, for instance, by professional intelligence or business analysts.

Discourse structure differences in lay and professional health communication

Purpose – In this paper the authors seek to compare lay (consumer) and professional (physician) discourse structures in answers to diabetes‐related questions in a public consumer health information website.Design/methodology/approach – Ten consumer and ten physician question threads were aligned. They generated 26 consumer and ten physician answers, constituting a total dataset of 717 discourse units (in sentences or sentence fragments). The authors depart from previous LIS health information behaviour research by utilizing a computational linguistics‐based theoretical framework of rhetorical structure theory, which enables research at the pragmatics level of linguistics in terms of the goals and effects of human communication.Findings – The authors reveal differences in discourse organization by identifying prevalent rhetorical relations in each type of discourse. Consumer answers included predominately (66 per cent) presentational rhetorical structure relations, those intended to motivate or otherwise h...

News in an online world: the need for an automatic crap detector

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 Hemingways “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.

Truth and Deception at the Rhetorical Structure Level

This paper furthers the development of methods to distinguish truth from deception in textual data. We use rhetorical structure theory (RST) as the analytic framework to identify systematic differences between deceptive and truthful stories in terms of their coherence and structure. A sample of 36 elicited personal stories, self‐ranked as truthful or deceptive, is manually analyzed by assigning RST discourse relations among each storys constituent parts. A vector space model (VSM) assesses each storys position in multidimensional RST space with respect to its distance from truthful and deceptive centers as measures of the storys level of deception and truthfulness. Ten human judges evaluate independently whether each story is deceptive and assign their confidence levels (360 evaluations total), producing measures of the expected human ability to recognize deception. As a robustness check, a test sample of 18 truthful stories (with 180 additional evaluations) is used to determine the reliability of our RST‐VSM method in determining deception. The contribution is in demonstration of the discourse structure analysis as a significant method for automated deception detection and an effective complement to lexicosemantic analysis. The potential is in developing novel discourse‐based tools to alert information users to potential deception in computer‐mediated texts.

Stretching conceptual structures in classifications across languages and cultures.

SUMMARY The authors describe the difficulties of translating classifications from a source language and culture to another language and culture. To demonstrate these problems, kinship terms and concepts from native speakers of fourteen languages were collected and analyzed to find differences between their terms and structures and those used in English. Using the representations of kinship terms in the Library of Congress Classification (LCC) and the Dewey Decimal Classification (DDC) as examples, the authors identified the source of possible lack of mapping between the domain of kinship in the fourteen languages studied and the LCC and DDC. Finally, some preliminary suggestions for how to make translated classifications more linguistically and culturally hospitable are offered.

Promoting serendipity online: recommendations for tool design

Some researchers have suggested that opportunities for serendipitous discovery of information may be limited in the online environment as a result of technological facilitation of information behavior. In response, they suggest building tools that enhance opportunities for serendipity. Based on our model of everyday serendipity, we offer design suggestions for tools that could enhance various conceptual facets of everyday serendipitous chance encounters.