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2023

Journal Articles

Orduña-Malea, Enrique; Bautista-Puig, Núria

Research assessment under debate: disentangling the interest around the DORA declaration on Twitter Journal Article

In: Scientometrics, 2023.

Abstract | BibTeX | Links:

Monaci, Sara; Mazali, Tatiana; Persico, Simone

Smart working during the Covid19 pandemic in Italy: Twitter narratives in female-centered communities Journal Article

In: Mediascapes Journal, vol. 21, iss. 1, pp. 323–343, 2023, ISSN: 2282-2542.

Abstract | BibTeX | Links:

@article{Monaci2023,

title = {Smart working during the Covid19 pandemic in Italy: Twitter narratives in female-centered communities},

author = {Sara Monaci and Tatiana Mazali and Simone Persico},

url = {https://rosa.uniroma1.it/rosa03/mediascapes/article/view/18290

https://rosa.uniroma1.it/rosa03/mediascapes/article/view/18290/17516},

issn = {2282-2542},

year  = {2023},

date = {2023-07-18},

urldate = {2023-07-18},

journal = {Mediascapes Journal},

volume = {21},

issue = {1},

pages = {323–343},

abstract = {While the recent pandemic has accelerated the spread of smart working dynamics in Italy, social media increased their importance as platforms to vehiculate information and points of view and shape public opinion. In the face of extended confinement and a looming health crisis, society has had to fundamentally rethink its daily work practices, social relations, family relationship management, and work-life balance. As a result, the radical and abrupt migration to networked platforms has been a disruptive and unprecedented phenomenon. We aimed to investigate the Twitter debate on smart working during the pandemic by focusing mainly on social concerns and thematics related to work-life balance by addressing the following research questions:

- RQ1: How was the topic of smart working debated on Twitter during the Covid19 pandemic (2020-2021) in Italy, and which narratives and issues fuelled the debate the most?

- RQ2: How the public debate has received the Italian government's work-life balance measures?

- RQ3: Which topics were most discussed by women on smart working?

We used Digital Methods to cope with re-proposing data to depict collective phenomena, social transformations, and cultural expressions by analyzing natively digital data on social media platforms. We gathered more than 750.000 tweets between 28 February 2020 and 30 November 2021, and we mapped narratives and communities by using social network analysis. This allowed for the selection of the more intriguing ones to define various sub-datasets on which to conduct a topic modeling study, which aided in understanding more nuanced aspects of the highly fragmented topic. By studying the italian debate, we identified specific communities which debated government measures to help families during the pandemic and discussed digitalization and smart working as a new paradigm for work. We found DAD (Didactic at Distance, aka homeschooling) as a transversal topic that highly affected how people experienced smart working.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

While the recent pandemic has accelerated the spread of smart working dynamics in Italy, social media increased their importance as platforms to vehiculate information and points of view and shape public opinion. In the face of extended confinement and a looming health crisis, society has had to fundamentally rethink its daily work practices, social relations, family relationship management, and work-life balance. As a result, the radical and abrupt migration to networked platforms has been a disruptive and unprecedented phenomenon. We aimed to investigate the Twitter debate on smart working during the pandemic by focusing mainly on social concerns and thematics related to work-life balance by addressing the following research questions:
- RQ1: How was the topic of smart working debated on Twitter during the Covid19 pandemic (2020-2021) in Italy, and which narratives and issues fuelled the debate the most?
- RQ2: How the public debate has received the Italian government's work-life balance measures?
- RQ3: Which topics were most discussed by women on smart working?
We used Digital Methods to cope with re-proposing data to depict collective phenomena, social transformations, and cultural expressions by analyzing natively digital data on social media platforms. We gathered more than 750.000 tweets between 28 February 2020 and 30 November 2021, and we mapped narratives and communities by using social network analysis. This allowed for the selection of the more intriguing ones to define various sub-datasets on which to conduct a topic modeling study, which aided in understanding more nuanced aspects of the highly fragmented topic. By studying the italian debate, we identified specific communities which debated government measures to help families during the pandemic and discussed digitalization and smart working as a new paradigm for work. We found DAD (Didactic at Distance, aka homeschooling) as a transversal topic that highly affected how people experienced smart working.

Skovgaard, Lea; Grundtvig, Anders

Who tweets what about personalised medicine? Promises and concerns from Twitter discussions in Denmark Journal Article

In: Digital Health, vol. 9, pp. 1-12, 2023.

Abstract | BibTeX | Links:

@article{Skovgaard2023,

title = {Who tweets what about personalised medicine? Promises and concerns from Twitter discussions in Denmark},

author = {Lea Skovgaard and Anders Grundtvig},

url = {https://journals.sagepub.com/doi/pdf/10.1177/20552076231169832},

doi = {10.1177/20552076231169832},

year  = {2023},

date = {2023-03-29},

urldate = {2023-03-29},

journal = {Digital Health},

volume = {9},

pages = {1-12},

abstract = {Digital health data are seen as valuable resources for the development of better and more efficient treatments, for instance through personalised medicine. However, health data are information about individuals who hold opinions and can challenge how data about them are used. Therefore it is important to understand public discussions around reuse of digital health data. Social media have been heralded as enabling new forms of public engagement and as a place to study social issues. In this paper, we study a public debate on Twitter about personalised medicine. We explore who participates in discussions about personalised medicine on Twitter and what they tweet about. Based on user-generated biographies we categorise users as having a ‘Professional interest in personalised medicine’ or as ‘Private’ users. We describe how users within the field tweet about the promises of personalised medicine, while users unaffiliated with the field tweet about the concrete realisation of these ambitions in the form of a new infrastructure and express concerns about the conditions for the implementation. Our study serves to remind people interested in public opinion that Twitter is a platform used for multiple purposes by different actors and not simply a bottom-up democratic forum. This study contributes with insights relevant to policymakers wishing to expand infrastructures for reuse of health data. First, by providing insights into what is discussed about health data reuse. Second, by exploring how Twitter can be used as a platform to study public discussions about reuse

of health data.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

2021

Masters Theses

Béchet, Nathalie

#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 Masters Thesis

2021.

Abstract | BibTeX | Links:

@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},

urldate = {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}

}

2020

PhD Theses

Gray, Daniel

Tweeting About Women: A Critical Discourse Analysis of International Women’s Day on Twitter PhD Thesis

School of Social Sciences, 2020.

Abstract | BibTeX | Links:

@phdthesis{Gray2020,

title = {Tweeting About Women:  A Critical Discourse Analysis of International Women’s Day on Twitter},

author = {Daniel Gray},

url = {https://orca.cardiff.ac.uk/id/eprint/137810/

https://orca.cardiff.ac.uk/id/eprint/137810/1/Thesis%20Daniel%20Gray%20Corrected%201-11-2020%282%29.pdf},

year  = {2020},

date = {2020-11-01},

urldate = {2020-11-01},

address = {Cardiff University, Cardiff, CF10 3AT},

school = {School of Social Sciences},

abstract = {This thesis is a work of critical digital sociology, investigating discourse which occurred on International Women’s Day 2017 (IWD2017) on Twitter, a widely used social media network, using innovative methodology. The principle finding presented in this thesis is methodological. I demonstrate that it is possible and productive to bring together qualitative analysis and so-called ‘big data’, specifically a large quantity of tweets, via innovative and original methodology, while preserving the unique and valuable affordances of critical, qualitative, theory-informed analysis.

Alongside demonstrating this, I also present a range of analytic findings related to the discourse I have analysed. The analytic findings include the use of popular and ‘fringe’ hashtags in linking mainstream and right-wing/reactionary topics, the prominence of anti- feminism and anti-Islam sentiment in discourse associated with supporters of US president Donald Trump, the antifeminist discursive splitting of feminism and feminists into benign and maligned categories, and the ways women are constructed by Twitter accounts representing police and armed forces.

Methodologically, this thesis provides a detailed account of the practicalities, challenges and strategies involved in approaching big social media data as a critical researcher using qualitative analysis. In doing so I argue that big social media data may be a fruitful area for qualitative work, but that in approaching it we should not discard our previous theoretical, analytical and ethical frameworks.},

keywords = {},

pubstate = {published},

tppubtype = {phdthesis}

}

2019

Online

Gauld, Christophe

Mining big data about representations of autism spectrum disorder : a comparison from Twitter to PubMed, a TwiMed proof-of-concept Online

2019.

Abstract | BibTeX | Links:

@online{Gauld2019b,

title = {Mining big data about representations of autism spectrum disorder : a comparison from Twitter to PubMed, a TwiMed proof-of-concept},

author = {Christophe Gauld},

url = {https://www.researchgate.net/publication/337289960_Mining_big_data_about_representations_of_autism_spectrum_disorder_a_comparison_from_Twitter_to_PubMed_a_TwiMed_proof-of-concept},

doi = {10.13140/RG.2.2.20575.61604},

year  = {2019},

date = {2019-11-15},

abstract = {Aim: Twitter is the most commonly used social media forum in public health and is considered the radio of the internet. Many health providers utilize this media to disseminate health information. Patient use of social media for mental health topics encourages providers to disseminate quality information and to develop virtual collaborative learning environments. Such social media could also be seen as a reflection of a trend towards folk psychology. This study explored trends in health information exchanged by users of Twitter, a broad social media, through analyses of tweets about Autism Spectrum Disorder (ASD). This proxy of trends in folk psychology could be compared semantically with the corpus derived from biomedical research. Methods: At first, we conducted a text-mining analysis with a sample of 10,000 tweets posted using #autism, by a text-mining method. We built a network of words in order to extract the main dimensions about these data (Latent Dirichlet Analysis). Second, we performed a geocoding analysis to create a Twitter maps of social media tweet and checked the regularity of tweets in the short and medium term. In parallel, we performed a text-mining analysis using the platform PubMed with the term « autis* », and we built networks of words. For each of them, we extracted the main dimensions from the terms. Results: We were able to retrieve 121,556 terms related to the term #autism. Most tweets focus on five dimensions: (1) Education, (2) Childhood, (3) Environment/Relatives, (4) Techniques/Sciences and (5) Support. Concerning the most researched topics in the biomedical research, on 49,021 publications, we found four dimensions: (I) Clinical/Neuropsychology/Psychometry, (II) Behavioral/Language aspects, (III) Neuroscience/Neurogenetics/Neuropharmacology, (IV) Comorbidities. Conclusion: Results suggest thematics about ASD disseminated between a social media and a biomedical database are really different. Health providers are encouraged to establish a presence on social media to learn about representations, share scholarly work or just exchange information with patients and relatives concerned by ASD.},

keywords = {},

pubstate = {published},

tppubtype = {online}

}

Aim: Twitter is the most commonly used social media forum in public health and is considered the radio of the internet. Many health providers utilize this media to disseminate health information. Patient use of social media for mental health topics encourages providers to disseminate quality information and to develop virtual collaborative learning environments. Such social media could also be seen as a reflection of a trend towards folk psychology. This study explored trends in health information exchanged by users of Twitter, a broad social media, through analyses of tweets about Autism Spectrum Disorder (ASD). This proxy of trends in folk psychology could be compared semantically with the corpus derived from biomedical research. Methods: At first, we conducted a text-mining analysis with a sample of 10,000 tweets posted using #autism, by a text-mining method. We built a network of words in order to extract the main dimensions about these data (Latent Dirichlet Analysis). Second, we performed a geocoding analysis to create a Twitter maps of social media tweet and checked the regularity of tweets in the short and medium term. In parallel, we performed a text-mining analysis using the platform PubMed with the term « autis* », and we built networks of words. For each of them, we extracted the main dimensions from the terms. Results: We were able to retrieve 121,556 terms related to the term #autism. Most tweets focus on five dimensions: (1) Education, (2) Childhood, (3) Environment/Relatives, (4) Techniques/Sciences and (5) Support. Concerning the most researched topics in the biomedical research, on 49,021 publications, we found four dimensions: (I) Clinical/Neuropsychology/Psychometry, (II) Behavioral/Language aspects, (III) Neuroscience/Neurogenetics/Neuropharmacology, (IV) Comorbidities. Conclusion: Results suggest thematics about ASD disseminated between a social media and a biomedical database are really different. Health providers are encouraged to establish a presence on social media to learn about representations, share scholarly work or just exchange information with patients and relatives concerned by ASD.

2018

Masters Theses

Pan, Ying-Ling

Understanding the message functions in health communication, promotion and pubic engagement on Twitter: An exploratory analysis of the SunSmart campaign Masters Thesis

University of Twente, Enschede, the Netherlands, 2018.

Abstract | BibTeX | Links:

@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}

}

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.

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2023

Journal Articles

2021

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2020

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2019

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2018

Masters Theses

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