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dc.contributor.authorZolfaghari, Amir Hossein-
dc.description.abstractIntroduction: A movement arose in the middle of a challenging pandemic time. In a year that everybody keeps their six feet distance and mask on, many came to the streets or started publishing social media contents asking for Black rights. It was after an injustice killing of a black man - George Floyd - by a police officer that #BlackLivesMatter trended again as the top conversation in the world. Hence, it became our question that how racism - specifically on social media - is associated with blacks' mattering lives. Methods: We carried out an ecological retrospective study on Twitter data for the year 2020, which had location tags inside the USA. We created inclusion criteria to shape our dataset based on that and categorizing tweets into separate groups. Our groups were (1) "BLM" for those supporting the "BlackLivesMatter" movement; (2) "Anti-BLM" containing tweets in opposition to the first group; (3) "Ambiguous" who had both previous group contents; and (4) the "Racists" comprising those who included offensive n-words in their tweets. We employed some statistical data by utilizing previous research for the "Life Expectancy", "Poverty Rates," "Educational Attainment," and "Race Compositions” factors of the black and white population in the USA by the states. We employed additional techniques to identify genders and classify records in reference to their states. Moreover, we applied the sentiment analysis using Python. We calculated the final rates considering each group's statistics compared to the sum of all tweets published in each state. The analysis of the final rates in correlation with employed tables was done by IBM SPSS Statistics 26. Results: We found 43,830,301 tweets with location data inside the USA in this time frame, and 306,925 of them applied for our study. A noticeable initial observation was the sharp increase of the #BlackLivesMatter after George Floyd's demise on May 25, 2020, while this hashtag has a history back to 2013. There is a positive correlation between the rates of offensive-content tweets and the life expectancy of Black males. The same tweets showed an association that wherever racism is higher, more are suffering poverty. This is rather surprising that the BlackLivesMatter movement supporters were mostly among those with the bachelor or advance degree educational attainments. By contrast, if a state had lower rates of high school degrees, more racists tweets exist there. The rates of aggressive tweets are higher in areas with more black populations and are weaker in states having white people's domination. Regarding the sentiment analysis, the majority of tweets are written in objective forms, and it had a slight increase after the mentioned event. The polarities were also mostly in a neutral way. The most negative sense belonged to the BLM supporters, with the rate of 46% before and 33% after the event. Conclusion: This project was undertaken to evaluate the relationship between rates of cyberracism and anti-racism posts to some real-world indicators. We considered our inclusion criteria in reference to the cruel killing of a black man – George Floyd - by police to investigate the published tweets classified by supporters and opponents of this story, in addition to those using offensive language towards black people. This study showed a strong correlation between these concepts while contents on the world wide web could impute the day-to-day life conversation. Hence, it shows how a drop in racist behaviors can lead to a world with higher life expectancy, wealth, and education. Reduction of color discrimination on social media and particularly toward blacks could help to have a healthier community. Contrarily, the rise of these bigoted contents results in disastrous consequences on these racialized populations.en_US
dc.subjectlife expectancyen_US
dc.subjecteducational attainmenten_US
dc.subjectcomposition ratesen_US
dc.title#BlackLivesMatter Movement and consequences of racism: a data and sentiment analysis on Tweets in the USAen_US
dc.description.degreeMaster of Science (MSc) in Computational Sciencesen_US
dc.publisher.grantorLaurentian University of Sudburyen_US
Appears in Collections:Computational Sciences - Master's theses

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