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020 _a9783031023248
_9978-3-031-02324-8
024 7 _a10.1007/978-3-031-02324-8
_2doi
050 4 _aTK5105.5-5105.9
072 7 _aUKN
_2bicssc
072 7 _aCOM043000
_2bisacsh
072 7 _aUKN
_2thema
082 0 4 _a004.6
_223
100 1 _aThelwall, Michael.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981029
245 1 0 _aWord Association Thematic Analysis
_h[electronic resource] :
_bA Social Media Text Exploration Strategy /
_cby Michael Thelwall.
250 _a1st ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aXVII, 111 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Information Concepts, Retrieval, and Services,
_x1947-9468
505 0 _aAcknowledgments -- Introduction -- Data Collection with Mozdeh -- Word Association Detection: Term Identification -- Word Association Contextualization: Term Meaning and Context -- Word Association Thematic Analysis: Theme Detection -- Word Association Thematic Analysis Examples -- Comparison Between WATA and Other Methods -- Ethics -- Project Planning -- Summary -- References -- Author Biography.
520 _aMany research projects involve analyzing sets of texts from the social web or elsewhere to get insights into issues, opinions, interests, news discussions, or communication styles. For example, many studies have investigated reactions to Covid-19 social distancing restrictions, conspiracy theories, and anti-vaccine sentiment on social media. This book describes word association thematic analysis, a mixed methods strategy to identify themes within a collection of social web or other texts. It identifies these themes in the differences between subsets of the texts, including female vs. male vs. nonbinary, older vs. newer, country A vs. country B, positive vs. negative sentiment, high scoring vs. low scoring, or subtopic A vs. subtopic B. It can also be used to identify the differences between a topic-focused collection of texts and a reference collection. The method starts by automatically finding words that are statistically significantly more common in one subset than another, thenidentifies the context of these words and groups them into themes. It is supported by the free Windows-based software Mozdeh for data collection or importing and for the quantitative analysis stages. This book explains the word association thematic analysis method, with examples, and gives practical advice for using it. It is primarily intended for social media researchers and students, although the method is applicable to any collection of short texts.
650 0 _aComputer networks .
_931572
650 1 4 _aComputer Communication Networks.
_981030
710 2 _aSpringerLink (Online service)
_981031
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031002311
776 0 8 _iPrinted edition:
_z9783031011962
776 0 8 _iPrinted edition:
_z9783031034527
830 0 _aSynthesis Lectures on Information Concepts, Retrieval, and Services,
_x1947-9468
_981032
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02324-8
912 _aZDB-2-SXSC
942 _cEBK
999 _c85090
_d85090