Tools and methods for capturing Twitter data during natural disasters

By Axel Bruns, Y. Liang. 2012

241 citations recorded by the source corpus.

During the course of several natural disasters in recent years, Twitter has been found to play an important role as an additional medium for many-to-many crisis communication. Emergency services are successfully using Twitter to inform the public about current developments, and are increasingly also attempting to source first-hand situational information from Twitter feeds (such as relevant hashtags). The further study of the uses of Twitter during natural disasters relies on the development of flexible and reliable research infrastructure for tracking and analysing Twitter feeds at scale and in close to real time, however. This article outlines two approaches to the development of such infrastructure: one which builds on the readily available open source platform yourTwapperkeeper to provide a low-cost, simple, and basic solution; and one which establishes a more powerful and flexible framework by drawing on highly scaleable, state-of-the-art technology.

Subfields: Communication, Sociology and Political Science

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Beyond aiding reproducibility and future comparisons, the availability of tools may also enable the participation of those lacking the resources to create their own (e.g., many researchers outside of computer science) (boyd and Crawford, 2012; Bruns and Liang, 2012).”
Instagrammatics and digital methods: studying visual social media, from selfies and GIFs to memes and emoji (2016, background): “Our starting point is in studying Instagram, building upon and moving beyond established research methods for Twitter analysis (Bruns & Burgess, 2011; Bruns & Liang, 2012) to examine a primarily visual platform.”
CrisisLex: A Lexicon for Collecting and Filtering Microblogged Communications in Crises (2014, background): “The first challenge in using microblog data is to retrieve comprehensive sets of disaster-related tweets (Bruns and Liang 2012).”
Using APIs for Data Collection on Social Media (2014, unknown): “ Pipes (2007), iScience Maps (Reips and Garaizar 2011) and YourTwapperkeeper (2011)) and for processing, analyzing, and visualizing data (e.g., Gawk (2011), Node XL (Smith et al, 2009) and Gephi (2011)) (Bruns and Liang 2012).”
Towards more systematic Twitter analysis: metrics for tweeting activities (2013, methodology): “A full discussion of capturing Twitter data around speci ﬁ c hashtags is beyond our scope (but see Bruns & Liang, 2012, for greater detail); a brief introduction to available tools, and to the data-sets they generate, is nonetheless useful as background.”
Social Media Analytics (2014, background): “Public officials could potentially use social media to identify situational information created by citizens in times of natural disasters (Bruns and Burgess 2012; Bruns and Liang 2012).”
Adaptive Co-attention Network for Named Entity Recognition in Tweets (2018, background): “2015) or natural disasters (Neubig et al. 2011; Bruns and Liang 2012), and mining disease outbreaks (Paul and Dredze 2011).”