Context Map Analysis of Fake News in Social Media: A Contextualized Visualization Approach

Şeref, Onur; Şeref, Michelle M. H.; Hong, Sukhwa

doi:10.24251/hicss.2020.299

Cited by 1 publication

(1 citation statement)

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“…This type of visual analytics supports the enhanced assessment of news articles from social media for explaining fake news using three frameworks (ATTN, MIMIC and PERT), which allow the users to analyse text information of news from different text analytics aspects. Another visual analytics study conducted by Seref et al (2020) presents text analytics based on a Context Map approach, which entails the visualisation of a connected network of n‐grams (a set of n consecutive words in a corpus) designed to identify similar fake news content from the perspective of text analytics.…”

Section: Related Workmentioning

confidence: 99%

A two‐step approach to detect and understand dismisinformation events occurring in social media: A case study with critical times

Yang

Chung

Singh

et al. 2023

Contingencies & Crisis Mgmt

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This article describes a novel two‐step approach of detecting and understanding dis/misinformation events in social media that occur during disasters and crisis events. To detect false news events, we designed a deep learning‐based detection algorithm and then trained it with a transfer learning scheme so that the algorithm could decide whether a given group of rumor‐related tweets is a dis/misinformation event. For understanding how dis/misinformation was diffused in social networks and identifying those who are responsible for creating and consuming false information, we present DismisInfoVis, which consists of various visualisations, including a social network graph, a map, line charts, pie charts, and bar charts. By integrating these deep learning and multi‐view visualisation techniques, we could gain a deeper insight into dis/misinformation events in social media from multiple angles. We describe in detail the implementation, training process, and performance evaluations of the detection algorithm and the design and utilization of DismisInfoVis for dis/misinformation data analyses. We hope that this study will contribute to improving the quality of information generated and shared on social media during critical times, eventually helping both the affected and the general public recover from the impacts of disasters and crisis events.

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Section: Related Workmentioning

confidence: 99%