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 (SVO) 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.
One approach taken by @cite_64 to understand widespread information on Twitter used manual content analysis. The approach utilised code frames for retweet content in order to categorise information flows (e.g., a report of an event), using the groupings to explore how people were using Twitter in the corresponding context. By using a variety of tweet codes, categories such as media reports, rumours (misinformation), and reactions were identified. In particular, rumours were identified as tweets where users had published content, without providing a reference (e.g., an external link).
@cite_64
1990474689
For social scientists, the widespread adoption of social media presents both an opportunity and a challenge. Data that can shed light on people’s habits, opinions and behaviour is available now on a scale never seen before, but this also means that it is impossible to analyse using conventional methodologies and tools. This article represents an experiment in applying a computationally assisted methodology to the analysis of a large corpus of tweets sent during the August 2011 riots in England.