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@mastersthesis{Béchet2021,

title = {#IamNotaVirus: text mining analysis of the blame phenomenon and anti-asian racism on Twitter amid the Covid-19 pandemic Observation of the narrative diversity generated by hashtag activism in France},

author = {Nathalie Béchet},

url = {https://www.researchgate.net/publication/349883313_IamNotaVirus_text_mining_analysis_of_the_blame_phenomenon_and_anti-asian_racism_on_Twitter_amid_the_Covid-19_pandemic_Observation_of_the_narrative_diversity_generated_by_hashtag_activism_in_France},

doi = {http://dx.doi.org/10.13140/RG.2.2.14680.21761},

year  = {2021},

date = {2021-05-01},

abstract = {The hashtag #JeNeSuisPasUnVirus (#IamNotAVirus) was coined in January 2020 during the outbreak of coronavirus (COVID-19) in China as anti-Asian racist incidents gained visibility nourished by the idea that if the pandemic originated in Asia, Asian people were infected and responsible for the spread of the virus. This hashtag reached a peak on January 28th before decreasing, following the shifting curve of the blame phenomenon (Atlani-Duault et al., 2020). Certainly anti-Asian racism is not a new phenomenon, but the Covid-19 pandemic came as an enhancer for xenophobic acts and hate speeches. As Asian communities informally got together online via hashtag activism to denounce persecutions they face, we could observe how the recurring blame process amid health crises, has been worded around ethnic and cultural stigmata. The many comparisons Twitter users from our corpus tended to make with anti-Muslim sentiments in France showed just how the phenomenon at stake here is the one of using a nation's minorities as a scapegoat for local issues. This 2020 epidemic and its associated Twitter hashtag #JeNeSuisPasUnVirus, are just a salience that ought to be grasped by researchers to scrutinize the plurality of narratives around anti-Asian racism and observe how the blame phenomenon works. The present study aims to do so by applying text mining methods to thousands of tweets containing this precise hashtag from the end of January to the end of March 2020.

The present article stands for a Master Thesis presented in order to obtain a M.A. in Data Sciences and Digital Sociology from Gustave Eiffel University under the supervision of Digital Sociology associate professor Bilel Benbouzid. It hasn't been published.},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{Pan2018,

title = {Understanding the message functions in health communication, promotion and pubic engagement on Twitter: An exploratory analysis of the SunSmart campaign},

author = {Ying-Ling Pan},

url = {https://essay.utwente.nl/76515/1/Pan_BA_faculty.pdf},

year  = {2018},

date = {2018-08-31},

address = {Enschede, the Netherlands},

school = {University of Twente},

abstract = {Background. As the mortality of skin cancer has risen rapidly over the recent decades, skin health  organisations  largely  use  social  media  as  a  communication  tool  to  promote  health campaigns and encourage participation. However, little is known about the specific approach to foster  engagement via tweets as a  form  of health  communication  and promote  health campaigns to engage the public. By focusing on the SunSmart skin health campaign on Twitter, this study aims to investigate how the communication during the campaign is characterised in terms of the functions of messages, to what extent the use of these messages can create public engagement, and how message contents play out among the functions. Methodology. By focusing on the SunSmart health campaign on Twitter, this study adopts a multi-method approach. First, a descriptive statistical analysis is used to understand whether levels  of  engagement  among  types  of  usersand  message  functions  differ. Second, Natural Language  Processing(NLP) is  adopted for  developing  a  codebook in  which four  message functions manifested from the SunSmart data are identified. Third, content analysis is used to manually  classify  each  tweet  to  different  user  types  and  message  functions. Last,  by  using Natural Language Processing(NLP) and the hashtag visualisation the matic analysis, we further explore whether the composition of content (i.e., keywords & thematic topics) among message functions differ. Results. Using the 2014 SunSmart health campaign on Twitter as an empirical context and on the basis of comparison between individuals and organisations(i.e.,the public), results show that individual users are more engaged in the SunSmart campaign on Twitter than organisations did. In addition, we find the  levels  of  engagement  among  the four  main  message  functions between individuals and organisations differ. At the content level, results show that utilisation of keywords  and  thematic  topics  among  different  message  functions generally differ among individuals and organisations. Contributions. This   study   offers   contributions   to   research   on media   studies,   health communication, and health campaign marketing. Practically, the results provides with insight on strategic health communication and marketing campaigns.},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}