This is The Geography of Hate – a cartographical appeal of every geotagged tweet in the continental U.S. between June 2012 and April 2013 in which the Holy Scripture “ Chinaman , ” “ gook , ” “ nigger , ” “ wetback , ” “ spic , ” “ dyke ” “ fag , ” “ homo , ” “ nance ” or “ cripple ” was used in an explicitly negative way .
make by the datavisualization experts atFloating Sheep , the synergistic map was made in response to literary criticism that a previous map – which plot the distribution of racial epithets in the wake of Obama ’s re - election – had get in at specious decision about the relative amount of racist message emanating from Mississippi and Alabama . ViaFloating Sheep :
for come up to [ one such critique ] , educatee at Humboldt State manually read and coded the sentiment of [ hundreds of one thousand of tweets containing homophobic , racist , or ableist slurs ] to determine if the give word was used in a positive , negative or neutral way . This reserve us to avert using any algorithmic opinion analytic thinking or innate oral communication processing , as many algorithms would have simply classified a tweet as ‘ negative ’ when the Logos was used in a neutral or positive direction . For example the phrase ‘ dyke ’ , while often negative when touch to an individual person , was also used in positive path ( e.g. “ dam on bikes # SFPride ” ) . The students were capable to recognise which were negative , neutral , or positive . Only those tweet used in an explicitly negative way are included in the map … All together , the students determined over 150,000 geotagged tweets with a hateful slur to be negative .
The image up top is the map of all the homophobic tweets deemed mean . Over at the interactive mathematical function , viewers can see like maps for antiblack and ableist tweets , and even parse the data point to examine the geographical distributions of individual word . The results were compelling . “ Even when normalized , ” write the research worker , “ many of the aspersion included in our analytic thinking display little meaningful spatial distribution ” :
For example , twinge referencing ‘ coon ’ are not concentrated in any individual plaza or region in the United States ; instead , quite depressingly , there are a number of pockets of absorption that present punishing custom of the word . In summation to looking at the density of hateful Christian Bible , we also probe how many unequaled drug user were twitch these words . For exemplar in the Quad Cities ( East Iowa ) 31 unique Twitter users tweeted the parole “ nigger ” in a hateful style 41 times . There are two likely reasons for eminent balance of such slurs in rural area : demographic differences and differing societal practices with regard to the use of Twitter . We will be testing the bunch of hate address against the demographic composition of an sphere in a belated phase of this project .
For the full experience , hold back outThe Geography of Hateinteractive map . To take more about the research , visit Floating Sheep .
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