The 'who' and 'what' of #diabetes on Twitter

Social media are being increasingly used for health promotion, yet the landscape of users, messages and interactions in such fora is poorly understood. Studies of social media and diabetes have focused mostly on patients, or public agencies addressing it, but have not looked broadly at all the participants or the diversity of content they contribute. We study Twitter conversations about diabetes through the systematic analysis of 2.5 million tweets collected over 8 months and the interactions between their authors. We address three questions: (1) what themes arise in these tweets?, (2) who are the most influential users?, (3) which type of users contribute to which themes? We answer these questions using a mixed-methods approach, integrating techniques from anthropology, network science and information retrieval such as thematic coding, temporal network analysis, and community and topic detection. Diabetes-related tweets fall within broad thematic groups: health information, news, social interaction, and commercial. At the same time, humorous messages and references to popular culture appear consistently, more than any other type of tweet. We classify authors according to their temporal 'hub' and 'authority' scores. Whereas the hub landscape is diffuse and fluid over time, top authorities are highly persistent across time and comprise bloggers, advocacy groups and NGOs related to diabetes, as well as for-profit entities without specific diabetes expertise. Top authorities fall into seven interest communities as derived from their Twitter follower network. Our findings have implications for public health professionals and policy makers who seek to use social media as an engagement tool and to inform policy design.Comment: 25 pages, 11 figures, 7 tables. Supplemental spreadsheet available from http://journals.sagepub.com/doi/suppl/10.1177/2055207616688841, Digital Health, Vol 3, 201

Mining the sociome for Health Informatics: Analysis of therapeutic lifestyle adherence of diabetic patients in Twitter

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.future.2020.04.025.Supplementary material 1: this file contains the 23 user communities detected using the GLay algorithm.In recent years, the number of active users in social media has grown exponentially. Despite the thematic diversity of the messages, social media have become an important vehicle to disseminate health information as well as to gather insights about patients experiences and emotional intelligence. Therefore, the present work proposes a new methodology of analysis to identify and interpret the behaviour, perceptions and appreciations of patients and close relatives towards a health condition through their social interactions. At the core of this methodology are techniques of natural language processing and machine learning as well as the reconstruction of knowledge graphs, and further graph mining. The case study is the diabetes community, and more specifically, the patients communicating about type 1 diabetes (T1D) and type 2 diabetes (T2D). The results produced in this study show the effectiveness of the proposed method to discover useful and non-trivial knowledge about patient perceptions of disease. Such knowledge may be used in the context of Health Informatics to promote healthy lifestyles in more efficient ways as well as to improve communication with the patients.This work was partially supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit, COMPETE 2020 (POCI-01-0145-FEDER-006684), the Xunta de Galicia (Centro singular de investigación de Galicia accreditation 2019–2022) and the European Union (European Regional Development Fund - ERDF)- Ref. ED431G2019/06, and Consellería de Educación, Universidades e Formación Profesional (Xunta de Galicia) under the scope of the strategic funding of ED431C2018/55-GRC Competitive Reference Group. The authors also acknowledge the Postdoc contract of Martín Pérez-Pérez, funded by the Xunta de Galicia.info:eu-repo/semantics/publishedVersio

A framework to extract biomedical knowledge from gluten-related tweets: the case of dietary concerns in digital era

Journal pre proofBig data importance and potential are becoming more and more relevant nowadays, enhanced by the explosive growth of information volume that is being generated on the Internet in the last years. In this sense, many experts agree that social media networks are one of the internet areas with higher growth in recent years and one of the fields that are expected to have a more significant increment in the coming years. Similarly, social media sites are quickly becoming one of the most popular platforms to discuss health issues and exchange social support with others. In this context, this work presents a new methodology to process, classify, visualise and analyse the big data knowledge produced by the sociome on social media platforms. This work proposes a methodology that combines natural language processing techniques, ontology-based named entity recognition methods, machine learning algorithms and graph mining techniques to: (i) reduce the irrelevant messages by identifying and focusing the analysis only on individuals and patient experiences from the public discussion; (ii) reduce the lexical noise produced by the different ways in how users express themselves through the use of domain ontologies; (iii) infer the demographic data of the individuals through the combined analysis of textual, geographical and visual profile information; (iv) perform a community detection and evaluate the health topic study combining the semantic processing of the public discourse with knowledge graph representation techniques; and (v) gain information about the shared resources combining the social media statistics with the semantical analysis of the web contents. The practical relevance of the proposed methodology has been proven in the study of 1.1 million unique messages from more than 400,000 distinct users related to one of the most popular dietary fads that evolve into a multibillion-dollar industry, i.e., gluten-free food. Besides, this work analysed one of the least research fields studied on Twitter concerning public health (i.e., the allergies or immunology diseases as celiac disease), discovering a wide range of health-related conclusions.SING group thanks CITI (Centro de Investigacion, Transferencia e Innovacion) from the University of Vigo for hosting its IT infrastructure. This work was supported by: the Associate Laboratory for Green Chemistry-LAQV, which is financed by national funds from and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of [UIDB/50006/2020] and [UIDB/04469/2020] units, and BioTecNorte operation [NORTE010145FEDER000004] funded by the European Regional Development Fund under the scope of Norte2020Programa Operacional Regional do Norte, the Xunta de Galicia (Centro singular de investigacion de Galicia accreditation 2019-2022) and the European Union (European Regional Development Fund - ERDF)- Ref. [ED431G2019/06] , and Conselleria de Educacion, Universidades e Formacion Profesional (Xunta de Galicia) under the scope of the strategic funding of [ED431C2018/55GRC] Competitive Reference Group. The authors also acknowledge the post-doctoral fellowship [ED481B2019032] of Martin PerezPerez, funded by the Xunta de Galicia. Funding for open access charge: Universidade de Vigo/CISUGinfo:eu-repo/semantics/publishedVersio

Topic modeling and social network analysis approach to explore diabetes discourse on Twitter in India

IntroductionThe utilization of social media presents a promising avenue for the prevention and management of diabetes. To effectively cater to the diabetes-related knowledge, support, and intervention needs of the community, it is imperative to attain a deeper understanding of the extent and content of discussions pertaining to this health issue. This study aims to assess and compare various topic modeling techniques to determine the most effective model for identifying the core themes in diabetes-related tweets, the sources responsible for disseminating this information, the reach of these themes, and the influential individuals within the Twitter community in India.MethodsTwitter messages from India, dated between 7 November 2022 and 28 February 2023, were collected using the Twitter API. The unsupervised machine learning topic models, namely, Latent Dirichlet Allocation (LDA), non-negative matrix factorization (NMF), BERTopic, and Top2Vec, were compared, and the best-performing model was used to identify common diabetes-related topics. Influential users were identified through social network analysis.ResultsThe NMF model outperformed the LDA model, whereas BERTopic performed better than Top2Vec. Diabetes-related conversations revolved around eight topics, namely, promotion, management, drug and personal story, consequences, risk factors and research, raising awareness and providing support, diet, and opinion and lifestyle changes. The influential nodes identified were mainly health professionals and healthcare organizations.DiscussionThe study identified important topics of discussion along with health professionals and healthcare organizations involved in sharing diabetes-related information with the public. Collaborations among influential healthcare organizations, health professionals, and the government can foster awareness and prevent noncommunicable diseases

Examining women’s persistence in stem: a mixed methods study of autonomy, competence, relatedness, and sociocultural influences on women in science, technology, engineering, and mathematics

Several decades of effort have improved the participation of women in science, technology, engineering, and mathematics (STEM), but the gender gap remains. Researchers have found diverse reasons for women’s underrepresentation in STEM, but less is understood about factors supporting persistence. This study’s purpose was to understand how women persist in STEM, through the lens of self-determination theory. Self-determination theory posits that persistence improves when one’s needs for autonomy, competence, and relatedness are satisfied. This embedded mixed methods study provides evidence of how autonomy, competence, relatedness, and sociocultural factors influence women’s persistence in STEM. Using network and snowball sampling, the researcher recruited 641 diverse women with 6+ years of STEM experience for an anonymous online survey. The instrument included the 24-item Basic Psychological Need Satisfaction and Frustration Scale (BPNSFS) and several open-ended questions. Statistical analyses resulted in findings of high satisfaction and low frustration levels of autonomy, competence, and relatedness among the STEM persisters. Competence was rated highest in satisfaction and relatedness rated lowest in frustration. Significant associations were found between persistence and the combined satisfaction of autonomy, competence, and relatedness, as well as for the satisfaction score for competence. Educational attainment level, race, living in a rural area, and occupation also showed significant associations with persistence. Thematic analyses of narrative responses revealed qualitative support for the BPNSFS results, including 17 satisfaction themes, with the most prevalent being social support, communal benefit, enjoyment, and self-efficacy. Ten frustration themes emerged, with the most prevalent being lack of relatedness and lack of knowledge. In addition to affirming the influence of autonomy, competence, and relatedness, participants’ narratives indicated six sociocultural themes, including discrimination and bias, and career and money. By integrating quantitative and qualitative findings, four conclusions were determined. First, that women in the study were highly satisfied overall, and second, that they have persisted despite negative experiences with discrimination and bias. Third, organizations must support women’s autonomy, competence, relatedness, and financial equity to promote persistence. Finally, there are career trajectory points where risk of attrition is more likely to occur. Recommendations include programs to promote women’s interest, self-efficacy, and belonging in STEM

Dementia, Identity, and the Role of Twitter

The narratives of people with dementia have traditionally been underrepresented in research, policy, and public life. The social networking site Twitter has the potential to affect the lived experiences of people with dementia and facilitate their social inclusion, but research is limited. The overarching aim of this thesis was to explore how and why people with dementia use Twitter. Study One was a content analysis of the profile descriptions of account holders who identified themselves as having dementia. The aims were to identify how many Twitter account holders identified themselves as having dementia, and to examine their characteristics. Study Two was a thematic analysis of the tweets of people with dementia. The aims were to explore how they used Twitter and to examine the illness narratives they created and promoted online. Study Three comprised thematic and longitudinal analyses of case studies of people with dementia. The aims were to examine, in their own words, how and why people with dementia used Twitter, and to explore how this changed over time. The findings of this thesis showed that people with dementia used Twitter to present themselves and their diagnosis in a positive manner. People with dementia used Twitter to have a voice on the issues that affect them, create social change, educate others, establish new social connections, expand existing offline social networks, access peer support, document experiences, communicate, and enhance feelings of self-worth. While the use of Twitter by people with dementia was largely positive, they also experienced technical difficulties, were vulnerable to online abuse, and found the platform increasingly difficult to use as the symptoms of dementia progressed. At present, Twitter might only be appropriate for people in the earlier stages of dementia. In the future, researchers could work with platform developers to make Twitter more dementia-friendly, develop guidelines for people with dementia on how to use and be safe on Twitter, and use longer timeframes to further examine how the use of Twitter by people with dementia changes as symptoms progress

Data curation for qualitative data reuse and big social research

In den letzten Jahren haben Innovationen bei Datenquellen und Methoden für die sozialwissenschaftliche Forschung zugenommen. Diese Forschungsarbeit zielt darauf ab, die Auswirkungen dieser Innovationen auf drei Praxisgemeinschaften besser zu verstehen: qualitativ Forschende, Big Social Data Forschende und Datenkurator*innen. Folgenden Forschungsfragen werden behandelt. RQ1: Wie unterscheidet sich die Kuratierung von Big Social Data und qualitativen Daten? RQ2: Welche Auswirkungen haben diese Ähnlichkeiten und Unterschiede auf die Kuratierung von Big Social Data und qualitativen Daten und was können wir aus der Kombination dieser beiden Communities lernen? Ich beantwortete diese Fragen durch eine Literaturrecherche, in der ich Gemeinsamkeiten zwischen qualitativer Datennachnutzung und Big Social Data identifizierte. Dann führte ich semi-strukturierte Interviews mit den drei Praxisgemeinschaften durch. Die Analyse identifizierte sechs Schlüsselthemen für die qualitative Datennachnutzung und Big Social Data: Kontext, Datenqualität und Vertrauenswürdigkeit, Datenvergleichbarkeit, informierte Einwilligung, Datenschutz und Vertraulichkeit sowie geistiges Eigentum und Dateneigentum. Ich habe außerdem fünf weitere Themen identifiziert: Domänenunterschiede, Strategien für eine verantwortungsvolle Praxis, Fragen der Datenpflege, Menschen oder Inhalte als Untersuchungsobjekte sowie unterschiedliche Schwerpunkte und Ansätze. Die Verbindung dieser drei Praxisgemeinschaften kann ein breiteres Verständnis der Schlüsselfragen unterstützen und zu verantwortungsbewussteren Forschungspraktiken führen. Datenkurator*innen verfügen über die Fähigkeiten und Perspektiven, um zwischen den Praxisgemeinschaften zu übersetzen und eine verantwortungsvolle qualitative Nachnutzung von Daten und Big Social Data zu unterstützen.Recent years have seen the rise of innovations in data sources and methods for social science research. This research aims to better understand the impact of these innovations on three communities of practice: qualitative researchers, big social researchers, and data curators. I address the following research questions. RQ1: How is big social data curation similar to and different from qualitative data curation? RQ1a: How are epistemological, ethical, and legal issues different or similar for qualitative data reuse and big social research? RQ1b: How can data curation practices support and resolve some of these epistemological and ethical issues? RQ2: What are the implications of these similarities and differences for big social data curation and qualitative data curation, and what can we learn from combining these two conversations? I answered these questions through a literature review, in which I identified issues in common between qualitative data reuse and big social research. Then I conducted semi-structured interviews with the three communities of practice. The research identified six key issues for qualitative data reuse and big social research: context, data quality and trustworthiness, data comparability, informed consent, privacy and confidentiality, and intellectual property and data ownership. I also identified five additional themes: domain differences, strategies for responsible practice, data curation issues, human subjects vs. content, and different focuses and approaches. Connecting these three communities of practice can support a broader understanding of the key issues and lead to more responsible research practices. Data curators have the skills and perspectives to translate between communities of practice and provide guidance for responsible qualitative data reuse and big social data

The 'who' and 'what' of #diabetes on Twitter

Social media are being increasingly used for health promotion, yet the landscape of users, messages and interactions in such fora is poorly understood. Studies of social media and diabetes have focused mostly on patients, or public agencies addressing it, but have not looked broadly at all the participants or the diversity of content they contribute. We study Twitter conversations about diabetes through the systematic analysis of 2.5 million tweets collected over 8 months and the interactions between their authors. We address three questions: (1) what themes arise in these tweets?; (2) who are the most in uential users?; (3) which type of users contribute to which themes? We answer these questions using a mixed-methods approach, integrating techniques from anthropology, network science and information retrieval such as thematic coding, temporal network analysis, and community and topic detection. Diabetes-related tweets fall within broad thematic groups: health information, news, social interaction, and commercial. At the same time, humorous messages and references to popular culture appear consistently, more than any other type of tweet. We classify authors according to their temporal `hub' and `authority' scores. Whereas the hub landscape is diffuse and uid over time, top authorities are highly persistent across time and comprise bloggers, advocacy groups and NGOs related to diabetes, as well as for-profit entities without specific diabetes expertise. Top authorities fall into seven interest communities as derived from their Twitter follower network. Our findings have implications for public health professionals and policy makers who seek to use social media as an engagement tool and to inform policy design