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Unsupervised quantification of entity consistency between photos and text in real-world news ...
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Müller-Budack, Eric. - : Hannover : Institutionelles Repositorium der Leibniz Universität Hannover, 2022
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Combining BERT with Contextual Linguistic Features for Identification of Propaganda Spans in News Articles
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In: Conference papers (2020)
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Context Map Analysis of Fake News in Social Media: A Contextualized Visualization Approach
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Detecting Media Bias in On-line News Articles: A Text Analytics Approach
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In: Electronic Theses and Dissertations (2018)
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Using semantic clustering to support situation awareness on Twitter: the case of world views
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Abstract:
In recent years, situation awareness has been recognised as a critical part of effective decision making, in particular for crisis management. One way to extract value and allow for better situation awareness is to develop a system capable of analysing a dataset of multiple posts, and clustering consistent posts into different views or stories (or, ‘world views’). However, this can be challenging as it requires an understanding of the data, including determining what is consistent data, and what data corroborates other data. Attempting to address these problems, this article proposes Subject-Verb-Object Semantic Suffix Tree Clustering (SVOSSTC) and a system to support it, with a special focus on Twitter content. The novelty and value of SVOSSTC is its emphasis on utilising the Subject–Verb–Object typology in order to construct semantically consistent world views, in which individuals—particularly those involved in crisis response—might achieve an enhanced picture of a situation from social media data. To evaluate our system and its ability to provide enhanced situation awareness, we tested it against existing approaches, including human data analysis, using a variety of real-world scenarios. The results indicated a noteworthy degree of evidence (e.g., in cluster granularity and meaningfulness) to affirm the suitability and rigour of our approach. Moreover, these results highlight this article’s proposals as innovative and practical system contributions to the research field.
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Keyword:
computational social science; crisis response; data clustering; fake news; information systems; social media analytics; user-support tools
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URL: https://doi.org/10.1186/s13673-018-0145-6
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Reaction to non-scheduled News During Financial Crisis: Australian Evidence
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