2022
Online
1.
Kamara, Abdourahmane Tintou; Vignes, Annick; Polleau, Auriane; Deschamps, Aurore; Caputo, Chiara; Prieur, Christophe; Egher, Claudia; Cubizolles, Dylan; Armanet, Enguerrand; Lucifora, Federico; Laglil, Hajar; Miotto, Marilù; Delivre, Raphael; Tadiello, Sébastien; Tuncer, Sila; Persico, Simone; Billard, Tom; Chabaux, Valentin; Tahiri, Zakaria
Crawling the italian Green Pass debate on Twitter Online
(DMI), The Digital Methods Initiative (Ed.): 2022, visited: 18.02.2022.
@online{Kamara2022,
title = {Crawling the italian Green Pass debate on Twitter},
author = {Abdourahmane Tintou Kamara and Annick Vignes and Auriane Polleau and Aurore Deschamps and Chiara Caputo and Christophe Prieur and Claudia Egher and Dylan Cubizolles and Enguerrand Armanet and Federico Lucifora and Hajar Laglil and Marilù Miotto and Raphael Delivre and Sébastien Tadiello and Sila Tuncer and Simone Persico and Tom Billard and Valentin Chabaux and Zakaria Tahiri},
editor = {The Digital Methods Initiative (DMI)},
url = {https://digitalmethods.net/Dmi/CrawlingItalianGreenPassDebate},
year = {2022},
date = {2022-02-18},
urldate = {2022-02-18},
abstract = {The last Censis report about the social situation in Italy highlighted that the number of people adhering to irrational thoughts is increasing and that this could be related to the pandemic situation. The term irrational describe the situation where people become victim of cognitive biases that could led them to wrong interpretations and conduct to conspiracy theories.
Social Networks are nowadays platforms that lot of users use to directly find informations about social interest topics and doing so they can be influenced by the contents that other people shares.
Twitter is a space where common people, VIPs, politics and journalists debate about actuality topics and sometime can become an highly polarizing environment. In Italy one of the most divisive topics since his institution in the middle of July has been the covid-19 certificate (commonly known as “green pass”).
The purpose of the project is to explore and classify the most polarizing contents surrounding this debate, particularly focusing on the external sources of informations shared into the platform.
A list of source domains will be crawled to map the network, in order to find links that can be used as sign of homophily between sources. The position into the debate will be evaluated according to proximity to known sources of information, previously labelled as “mainstream” or “not mainstream” with the aim of revealing the possible presence of platforms related to conspiracies in the sources far from mainstream media.},
keywords = {},
pubstate = {published},
tppubtype = {online}
}
The last Censis report about the social situation in Italy highlighted that the number of people adhering to irrational thoughts is increasing and that this could be related to the pandemic situation. The term irrational describe the situation where people become victim of cognitive biases that could led them to wrong interpretations and conduct to conspiracy theories.
Social Networks are nowadays platforms that lot of users use to directly find informations about social interest topics and doing so they can be influenced by the contents that other people shares.
Twitter is a space where common people, VIPs, politics and journalists debate about actuality topics and sometime can become an highly polarizing environment. In Italy one of the most divisive topics since his institution in the middle of July has been the covid-19 certificate (commonly known as “green pass”).
The purpose of the project is to explore and classify the most polarizing contents surrounding this debate, particularly focusing on the external sources of informations shared into the platform.
A list of source domains will be crawled to map the network, in order to find links that can be used as sign of homophily between sources. The position into the debate will be evaluated according to proximity to known sources of information, previously labelled as “mainstream” or “not mainstream” with the aim of revealing the possible presence of platforms related to conspiracies in the sources far from mainstream media.
Social Networks are nowadays platforms that lot of users use to directly find informations about social interest topics and doing so they can be influenced by the contents that other people shares.
Twitter is a space where common people, VIPs, politics and journalists debate about actuality topics and sometime can become an highly polarizing environment. In Italy one of the most divisive topics since his institution in the middle of July has been the covid-19 certificate (commonly known as “green pass”).
The purpose of the project is to explore and classify the most polarizing contents surrounding this debate, particularly focusing on the external sources of informations shared into the platform.
A list of source domains will be crawled to map the network, in order to find links that can be used as sign of homophily between sources. The position into the debate will be evaluated according to proximity to known sources of information, previously labelled as “mainstream” or “not mainstream” with the aim of revealing the possible presence of platforms related to conspiracies in the sources far from mainstream media.
2021
Masters Theses
2.
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.
@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}
}
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.
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.
2020
PhD Theses
3.
Gray, Daniel
Tweeting About Women: A Critical Discourse Analysis of International Women’s Day on Twitter PhD Thesis
School of Social Sciences, 2020.
@phdthesis{Gray2020,
title = {Tweeting About Women: A Critical Discourse Analysis of International Women’s Day on Twitter},
author = {Daniel Gray},
url = {https://www.cardiff.ac.uk/people/research-students/view/1057519-gray-daniel},
year = {2020},
date = {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}
}
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.
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.
2019
Masters Theses
4.
Aumüller-Wagner, Sophia; Læarsson, Bartal; Agar, Muhammed; Mituzaite, Kristina; Münz, Janina; Malnača, Linda
Dominant and silent voices in the discourse on Health Smart Homes for the Elderly on Twitter and Scopus Masters Thesis
IT-University of Copenhagen, 2019.
@mastersthesis{Aumüller-Wagner2019,
title = {Dominant and silent voices in the discourse on Health Smart Homes for the Elderly on Twitter and Scopus},
author = {Sophia Aumüller-Wagner and Bartal Læarsson and Muhammed Agar and Kristina Mituzaite and Janina Münz and Linda Malnača},
url = {https://www.dasts.dk/wp-content/uploads/ESPS_Health-smart-home-for-the-elderly.pdf},
year = {2019},
date = {2019-09-01},
school = {IT-University of Copenhagen},
abstract = {Living in a world of demographic change means that aging societies will to a higher extend deal with mobility issues, chronic illnesses, and fall risks. Smart home technologies are increasingly considered in healthcare and could help alleviate the dilemma of people wanting to remain autonomous and independent while growing older. Under the lens of Clarke’s Situational Analysis, this paper explores how health smart homes for the elderly are co-constructed on the platforms of Scopus and Twitter. Using both Big Data and Thick Data, silent voices in the discourse around this technology are uncovered in order to understand how far the innovation of smart homes is aligned with the needs of its users. The main finding of this research paper shows that the field around health smart homes for the elderly is still in its early stages and the needs and concerns of elderly people are not yet considered enough. In order to design smart health homes for the elderly, the researchers propose a requirement pyramid that can aid the design of these future technologies. },
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Living in a world of demographic change means that aging societies will to a higher extend deal with mobility issues, chronic illnesses, and fall risks. Smart home technologies are increasingly considered in healthcare and could help alleviate the dilemma of people wanting to remain autonomous and independent while growing older. Under the lens of Clarke’s Situational Analysis, this paper explores how health smart homes for the elderly are co-constructed on the platforms of Scopus and Twitter. Using both Big Data and Thick Data, silent voices in the discourse around this technology are uncovered in order to understand how far the innovation of smart homes is aligned with the needs of its users. The main finding of this research paper shows that the field around health smart homes for the elderly is still in its early stages and the needs and concerns of elderly people are not yet considered enough. In order to design smart health homes for the elderly, the researchers propose a requirement pyramid that can aid the design of these future technologies.
Online
5.
Gauld, Christophe
Mining big data about representations of autism spectrum disorder : a comparison from Twitter to PubMed, a TwiMed proof-of-concept Online
2019.
@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
6.
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.
@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.
Online
7.
Karsgaard, Carrie; Bainotti, Lucia; Nero, Serena Del; Flaim, Giacomo; Hockenhull, Michael; MacDonald, Maggie; Martella, Antonio; Valderrama, Erika; Valerio, Gabriel
Canadian Pipeline Politics: Mapping (visual) discourse in platform spaces Online
(DMI), The Digital Methods Initiative (Ed.): 2018, visited: 02.08.2018.
@online{Karsgaard2018,
title = {Canadian Pipeline Politics: Mapping (visual) discourse in platform spaces},
author = {Carrie Karsgaard and Lucia Bainotti and Serena Del Nero and Giacomo Flaim and Michael Hockenhull and Maggie MacDonald and Antonio Martella and Erika Valderrama and Gabriel Valerio},
editor = {The Digital Methods Initiative (DMI)},
url = {https://digitalmethods.net/Dmi/SummerSchool2018PipelinePolitics
https://docs.google.com/presentation/d/1ieIJDHakx44Glre3VRdJu2Z6o60is22aIJoRobw6YF4/edit#slide=id.p},
year = {2018},
date = {2018-08-02},
urldate = {2018-08-02},
abstract = {Methodological findings: Twitter
Twitter’s free-form user location field enables political expression where location-identifiers have political significance; in the Canadian landscape, for instance, users may identify according to Indigenous place names rather than officially sanctioned place names.
The implication for digital methods research is that user location provides a means of exploring issue alignment and political stance for issues with geographical components.
Methodological findings: Instagram
An analytical challenge - but also an opportunity - is posed by Instagram’s multiple discursive spaces - images, text, and hashtags - as they are variously used over time. The project prototypes a comparative approach to these multiple spaces through analysis of each through multiple time slices, including analysis of the linkages between various discourses where possible (i.e. by hashtag-image and text-image analysis). Included is a new approach to text-image analysis using Cortext, which allows exploration of the user-generated text in Instagram posts, beyond what is available via hashtag analysis.
Taken together, multiple maps reinforce certain issue patterns through their repeated representation in various visualizations; at the same time, individual maps reveal nuances of the issue that only emerge through a single discourse (whether visual, textual, or connective via hashtags). This project thus demonstrates how critical discourse analysis and visual analysis may be conducted at multiple and intersecting levels through a critical cartographical approach, enabling a more robust understanding of the issue as it is performed online.
Substantive Findings
Our findings indicate that when analyzed as above, the tools embedded in both Twitter and Instagram allow us to infer discursive alignment with issue positioning, not only for/against the key issue, but also within sub-groups, allowing a nuanced view of the issue. For instance, within anti-pipeline sentiment, analysis of locations, hashtags, text, and images reveals competing ideals between protection of the land (as pristine), ownership of the land (as a Vancouver resident), and stewardship of the land (as already occupied by Indigenous peoples). By tracing these discursive groups over time, we see increasing overlap within our issue network visualizations, where distinct clusters are replaced by heterogenous networks, indicating that the pipeline issue may function as a boundary object, bringing various publics closer together.},
keywords = {},
pubstate = {published},
tppubtype = {online}
}
Methodological findings: Twitter
Twitter’s free-form user location field enables political expression where location-identifiers have political significance; in the Canadian landscape, for instance, users may identify according to Indigenous place names rather than officially sanctioned place names.
The implication for digital methods research is that user location provides a means of exploring issue alignment and political stance for issues with geographical components.
Methodological findings: Instagram
An analytical challenge - but also an opportunity - is posed by Instagram’s multiple discursive spaces - images, text, and hashtags - as they are variously used over time. The project prototypes a comparative approach to these multiple spaces through analysis of each through multiple time slices, including analysis of the linkages between various discourses where possible (i.e. by hashtag-image and text-image analysis). Included is a new approach to text-image analysis using Cortext, which allows exploration of the user-generated text in Instagram posts, beyond what is available via hashtag analysis.
Taken together, multiple maps reinforce certain issue patterns through their repeated representation in various visualizations; at the same time, individual maps reveal nuances of the issue that only emerge through a single discourse (whether visual, textual, or connective via hashtags). This project thus demonstrates how critical discourse analysis and visual analysis may be conducted at multiple and intersecting levels through a critical cartographical approach, enabling a more robust understanding of the issue as it is performed online.
Substantive Findings
Our findings indicate that when analyzed as above, the tools embedded in both Twitter and Instagram allow us to infer discursive alignment with issue positioning, not only for/against the key issue, but also within sub-groups, allowing a nuanced view of the issue. For instance, within anti-pipeline sentiment, analysis of locations, hashtags, text, and images reveals competing ideals between protection of the land (as pristine), ownership of the land (as a Vancouver resident), and stewardship of the land (as already occupied by Indigenous peoples). By tracing these discursive groups over time, we see increasing overlap within our issue network visualizations, where distinct clusters are replaced by heterogenous networks, indicating that the pipeline issue may function as a boundary object, bringing various publics closer together.
Twitter’s free-form user location field enables political expression where location-identifiers have political significance; in the Canadian landscape, for instance, users may identify according to Indigenous place names rather than officially sanctioned place names.
The implication for digital methods research is that user location provides a means of exploring issue alignment and political stance for issues with geographical components.
Methodological findings: Instagram
An analytical challenge - but also an opportunity - is posed by Instagram’s multiple discursive spaces - images, text, and hashtags - as they are variously used over time. The project prototypes a comparative approach to these multiple spaces through analysis of each through multiple time slices, including analysis of the linkages between various discourses where possible (i.e. by hashtag-image and text-image analysis). Included is a new approach to text-image analysis using Cortext, which allows exploration of the user-generated text in Instagram posts, beyond what is available via hashtag analysis.
Taken together, multiple maps reinforce certain issue patterns through their repeated representation in various visualizations; at the same time, individual maps reveal nuances of the issue that only emerge through a single discourse (whether visual, textual, or connective via hashtags). This project thus demonstrates how critical discourse analysis and visual analysis may be conducted at multiple and intersecting levels through a critical cartographical approach, enabling a more robust understanding of the issue as it is performed online.
Substantive Findings
Our findings indicate that when analyzed as above, the tools embedded in both Twitter and Instagram allow us to infer discursive alignment with issue positioning, not only for/against the key issue, but also within sub-groups, allowing a nuanced view of the issue. For instance, within anti-pipeline sentiment, analysis of locations, hashtags, text, and images reveals competing ideals between protection of the land (as pristine), ownership of the land (as a Vancouver resident), and stewardship of the land (as already occupied by Indigenous peoples). By tracing these discursive groups over time, we see increasing overlap within our issue network visualizations, where distinct clusters are replaced by heterogenous networks, indicating that the pipeline issue may function as a boundary object, bringing various publics closer together.
8.
Emambakhsh, T.; Andreatta, B. Da Fonseca; Pan, C.; Rico, S.
From Hollywood to Bollywood, the rise of the #metoo movement in the Indian Twitter sphere Online
Po, Medialab Science (Ed.): 2018, visited: 01.01.2018.
BibTeX | Links:
@online{Emambakhsh2018,
title = {From Hollywood to Bollywood, the rise of the #metoo movement in the Indian Twitter sphere},
author = {T. Emambakhsh and B. Da Fonseca Andreatta and C. Pan and S. Rico},
editor = {Medialab Science Po},
url = {https://fonio.medialab.sciences-po.fr/thinkdolphin/read/4004953c-4796-4a64-bbf9-962179684086?lang=en},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
keywords = {},
pubstate = {published},
tppubtype = {online}
}
2016
Book Chapters
9.
Fausto, Sibele; Aventurier, Pascal
Scientific Literature on Twitter as a subject research: findings based on bibliometric analysis Book Chapter
In: Handbook Twitter For Research 2015 – 2016, pp. 242, EMLYON Press, 2016.
@inbook{fausto2016scientific,
title = {Scientific Literature on Twitter as a subject research: findings based on bibliometric analysis},
author = {Sibele Fausto and Pascal Aventurier},
url = {https://hal.archives-ouvertes.fr/hal-01297804/},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
booktitle = {Handbook Twitter For Research 2015 – 2016},
pages = {242},
publisher = {EMLYON Press},
abstract = {Since its launch in 2006, Internet platform Twitter has rapidly expanded. As a phenomenon of the digital era, Twitter generates a new type of research data that has received a good deal of attention in the academic literature. It has turned into a popular subject research that has been widely investigated in the academic world in different fields ranging from the Social Sciences to Health Sciences, addressing various questions, methods approaches, and covering multiple data sets. This study provides some findings of a bibliometric study which was conducted to describe the scientific literature available on Twitter with descriptive, quantitative information and also in a qualitative approach, in addition to the previous studies and designed as a contribution to a broader picture of how the evolution of the current scientific literature about Twitter is related to bibliographic data sets. Results show a variety of findings that can provide a better comprehension of this social media platform which evolved from a data source for the research to, nowadays, being a research subject itself },
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
Since its launch in 2006, Internet platform Twitter has rapidly expanded. As a phenomenon of the digital era, Twitter generates a new type of research data that has received a good deal of attention in the academic literature. It has turned into a popular subject research that has been widely investigated in the academic world in different fields ranging from the Social Sciences to Health Sciences, addressing various questions, methods approaches, and covering multiple data sets. This study provides some findings of a bibliometric study which was conducted to describe the scientific literature available on Twitter with descriptive, quantitative information and also in a qualitative approach, in addition to the previous studies and designed as a contribution to a broader picture of how the evolution of the current scientific literature about Twitter is related to bibliographic data sets. Results show a variety of findings that can provide a better comprehension of this social media platform which evolved from a data source for the research to, nowadays, being a research subject itself
LIST OF SCIENTIFIC WORKS THAT HAVE USED CORTEXT MANAGER
(Sources: Google Scholar, HAL, Scopus, WOS and search engines)
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We seek to understand how the scientific production that used CorText Manager has evolved and to characterise it. You will find here our analysis of this scientific production.
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