Does Fake News in Different Languages Tell the Same Story? An Analysis of Multi-level Thematic and Emotional Characteristics of News about COVID-19

2023 | Information Systems Frontiers | Citations: 0

Authors: Zhou, Lina; Tao, Jie; Zhang, Dongsong

Abstract: Fake news is being generated in different languages, yet existing studies are do ... Expand

Abstract: Fake news is being generated in different languages, yet existing studies are dominated by English news. The analysis of fake news content has focused on lexical and stylometric features, giving little attention to semantic features. A few studies involving semantic features have either used them as the inputs to classifiers with no interpretations, or treated them in isolation. This research aims to investigate both thematic and emotional characteristics of fake news at different levels and compare them between different languages for the first time. It extends a state-of-the-art topic modeling technique to extract news topics and introduces a divergence measure to assess the importance of thematic characteristics for identifying fake news. We further examine associations of the thematic and emotional characteristics of fake news. The empirical findings have implications for developing both general and language-specific countermeasures for fake news. Collapse

Topics: fake news pandemic Twitter social media internet technology

Methods: topic model descriptive statistic transformer model Student's t-test literature study

Finding Useful Solutions in Online Knowledge Communities: A Theory-Driven Design and Multilevel Analysis

2020 | Information Systems Research | Citations: 0

Authors: Liu, Xiaomo; Wang, G. Alan; Fan, Weiguo; Zhang, Zhongju

Abstract: Online communities and social collaborative platforms have become an increasingl ... Expand

Abstract: Online communities and social collaborative platforms have become an increasingly popular avenue for knowledge sharing and exchange. In these communities, users often engage in informal conversations responding to questions and answers, and over time, they produce a huge amount of highly unstructured and implicit knowledge. How to effectively manage the knowledge repository and identify useful solutions thus becomes a major challenge. In this study, we propose a novel text analytic framework to extract important features from online forums and apply them to classify the usefulness of a solution. Guided by the design science research paradigm, we utilize a kernel theory of the knowledge adoption model, which captures a rich set of argument quality and source credibility features as the predictors of information usefulness. We test our framework on two large-scale knowledge communities: the Apple Support Community and Oracle Community. Our extensive analysis and performance evaluation illustrate that the proposed framework is both effective and efficient in predicting the usefulness of solutions embedded in the knowledge repository. We highlight the theoretical implications of the study as well as the practical applications of the framework to other domains. Collapse

Topics: Oracle database online community information retrieval problem solving knowledge repository

Methods: computational algorithm machine learning natural language processing classification method experiment

Theories: behavioral theory