Literature DB >> 32497125

The geography of sentiment towards the Women's March of 2017.

Diane H Felmlee1,2, Justine I Blanford3,4, Stephen A Matthews1,2,5, Alan M MacEachren3.   

Abstract

The Women's March of 2017 generated unprecedented levels of participation in the largest, single day, protest in history to date. The marchers protested the election of President Donald Trump and rallied in support of several civil issues such as women's rights. "Sister marches" evolved in at least 680 locations across the United States. Both positive and negative reactions to the March found their way into social media, with criticism stemming from certain, conservative, political sources and other groups. In this study, we investigate the extent to which this notable, historic event influenced sentiment on Twitter, and the degree to which responses differed by geographic area within the continental U.S. Tweets about the event rose to an impressive peak of over 12% of all geo-located tweets by mid-day of the March, Jan. 21. Messages included in tweets associated with the March tended to be positive in sentiment, on average, with a mean of 0.34 and a median of 0.07 on a scale of -4 to +4. In fact, tweets associated with the March were more positive than all other geo-located tweets during the day of the March. Exceptions to this pattern of positive sentiment occurred only in seven metropolitan areas, most of which involved very small numbers of tweets. Little evidence surfaced of extensive patterns of negative, aggressive messages towards the event in this set of tweets. Given the widespread nature of online harassment and sexist tweets, more generally, the results are notable. In sum, online reactions to the March on this social media platform suggest that this modern arm of the Women's Movement received considerable, virtual support across the country.

Entities:  

Year:  2020        PMID: 32497125     DOI: 10.1371/journal.pone.0233994

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  1 in total

1.  Exploring Descriptions of Movement Through Geovisual Analytics.

Authors:  Scott Pezanowski; Prasenjit Mitra; Alan M MacEachren
Journal:  KN J Cartogr Geogr Inf       Date:  2022-02-24
  1 in total

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