Published
Edited
Nov 1, 2019
1 star
md`# Thinking about my Project`
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<h3>
the idea
</h3>
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I hope to explore the origin of the phrase "cancel culture" and how it is used on Twitter (and possibly also on Youtube). I'm interested in answering the questions:
- What political affiliations do people who talk negatively and positively about "cancel culture" tend to have?
- What sociopolitical topics are commonly referenced in tweets that discuss "cancel culture"?
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To answer these questions, I'll have to use some kind of scraping tool such as [NodeXL Pro](https://www.smrfoundation.org/nodexl/) that gets data from the TwitterAPI. For a first round of data gathering, I would take data from a search for "cancel culture" probably between certain dates ("until:2017-12-31 since:2017-07-01" for example) and then load this search into topic modeling and lexical clustering algorithms to find out what phrases are commonly associated with instances of "cancel culture". I would also want to look into how I might aggregate tweets from specific users who tweet about "cancel culture" to find out how they locate themselves with respect to political ideologies like feminism, conservatism, transformative justice, etc.
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#### graphical representations
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I would probably want to have some kind of slideshow-esque widget that provides time-sensitive anonymized examples of "what people are saying about 'cancel culture'." I would also want to have some kind of chart that aligns uses and treatments of "cancel culture" with users' treament of other topics.
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<script type="text/javascript" src="https://ssl.gstatic.com/trends_nrtr/1982_RC01/embed_loader.js"></script>
<script type="text/javascript">
trends.embed.renderExploreWidget("TIMESERIES", {"comparisonItem":[{"keyword":"cancel culture","geo":"US","time":"today 5-y"}],"category":0,"property":""}, {"exploreQuery":"date=today%205-y&geo=US&q=cancel%20culture","guestPath":"https://trends.google.com:443/trends/embed/"});
</script>
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#### potential sources for methods:
- [Open Extraction of Fine-Grained Political Statements](https://www.aclweb.org/anthology/D15-1008.pdf)
- [Stanford Parser](https://nlp.stanford.edu/software/lex-parser.shtml)
- [UCBerkeley article on Computational Text Analysis](https://matrix.berkeley.edu/research/tips-computational-text-analysis)
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