Understanding Discussions of Health Issues on Twitter: A Visual Analytic Study

A role for qualitative methods in researching Twitter data on a popular science article's communication

Big Data communication researchers have highlighted the need for qualitative analysis of online science conversations to better understand their meaning. However, a scholarly gap exists in exploring how qualitative methods can be applied to small data regarding micro-bloggers' communications about science articles. While social media attention assists with article dissemination, qualitative research into the associated microblogging practices remains limited. To address these gaps, this study explores how qualitative analysis can enhance science communication studies on microblogging articles. Calls for such qualitative approaches are supported by a practical example: an interdisciplinary team applied mixed methods to better understand the promotion of an unorthodox but popular science article on Twitter over a 2-year period. While Big Data studies typically identify patterns in microbloggers' activities from large data sets, this study demonstrates the value of integrating qualitative analysis to deepen understanding of these interactions. In this study, a small data set was analyzed using NVivo™ by a pragmatist and MAXQDA™ by a statistician. The pragmatist's multimodal content analysis found that health professionals shared links to the article, with its popularity tied to its role as a communication event within a longstanding debate in the health sciences. Dissident professionals used this article to support an emergent paradigm. The analysis also uncovered practices, such as language localization, where a title was translated from English to Spanish to reach broader audiences. A semantic network analysis confirmed that terms used by the article's tweeters strongly aligned with its content, and the discussion was notably pro-social. Meta-inferences were then drawn by integrating the findings from the two methods. These flagged the significance of contextualizing the sharing of a health science article in relation to tweeters' professional identities and their stances on health-related issues. In addition, meta-critiques highlighted challenges in preparing accurate tweet data and analyzing them using qualitative data analysis software. These findings highlight the valuable contributions that qualitative research can make to research involving microblogging data in science communication. Future research could critique this approach or further explore the microblogging of key articles within important scientific debates.

Examining the Prevailing Negative Sentiments Surrounding Measles Vaccination: Unsupervised Deep Learning of Twitter Posts from 2017 to 2022

Despite the proven safety and clinical efficacy of the Measles vaccine, many countries are seeing new heights of vaccine hesitancy or refusal, and are experiencing a resurgence of measles infections as a consequence. With the use of novel machine learning tools, we investigated the prevailing negative sentiments related to Measles vaccination through an analysis of public Twitter posts over a 5-year period. We extracted original tweets using the search terms related to "measles" and "vaccine," and posted in English from January 1, 2017, to December 15, 2022. Of these, 155,363 tweets were identified to be negative sentiment tweets from unique individuals, through the use of Bidirectional Encoder Representations from Transformers (BERT) Named Entity Recognition and SieBERT, a pretrained sentiment in English analysis model. This was followed by topic modeling and qualitative thematic analysis performed inductively by the study investigators. A total of 11 topics were generated after applying BERTopic. To facilitate a global discussion of results, the topics were grouped into four different themes through iterative thematic analysis. These include (a) the rejection of "anti-vaxxers" or antivaccine sentiments, (b) misbeliefs and misinformation regarding Measles vaccination, (c) negative transference due to COVID-19 related policies, and (d) public reactions to contemporary Measles outbreaks. Theme 1 highlights that the current public discourse may further alienate those who are vaccine hesitant because of the disparaging language often used, while Themes 2 and 3 highlight the typology of misperceptions and misinformation underlying the negative sentiments related to Measles vaccination and the psychological tendency of disconfirmation bias. Nonetheless, the analysis was based solely on Twitter and only tweets in English were included; hence, the findings may not necessarily generalize to non-Western communities. It is important to further understand the thinking and feeling of those who are vaccine hesitant to address the issues at hand.

A Little Bird Told Me… Nutri-Score Panoramas from a Flight over Europe, Connecting Science and Society

Within the Farm to Fork Strategy, the European Commission ask for a unified Front Of Pack nutritional label for food to be used at the European level. The scientific debate identified the Nutri-Score (NS) as the most promising candidate, but within the political discussion, some Member States brought to attention several issues related to its introduction. This misalignment led to a postponement of the final decision. With the aim to shed some light on the current stances and contribute to the forthcoming debate, the objective of the present work is to understand to what extent scientific research addresses the issues raised by the general public. We applied a structural topic model to tweets from four European countries (France, Germany, Italy, Spain) and to abstracts of scientific papers, all dealing with the NS topic. Different aspects of the NS debate are discussed in different countries, but scientific research, while addressing some of them (e.g., the comparison between NS and other labels), disregards others (e.g., relations between NS and traditional products). It is advisable, therefore, to widen the scope of NS research to properly address the concerns of European society and to provide policymakers with robust evidence to support their decisions.

Analysis of Content, Social Networks, and Sentiment of Front-of-Pack Nutrition Labeling in the European Union on Twitter

In recent years, concerted political efforts have been made at the national and European Union (EU) level to promote the consumption of healthy foods. The European Commission (EC) expressed the need for a harmonized and mandatory front-of-pack nutrition labeling (FOPL) system at the EU level. The EC will adopt the proposal by the end of 2022. Our research work aims to understand the public discourse on FOPL in the EU via Twitter, by analyzing tweet content, sentiment, and mapping network characteristics. Tweet search and data collection were performed using the Twitter application programming interface (API), with no time or language restrictions. The content was coded with the QRS Nvivo software package and analyzed thematically. Automatic sentiment analysis was performed with QSR Nvivo, and network analysis was performed with Gephi 0.9.2. A total of 4,073 tweets were posted, mostly from the UK, Spain, and France. Themes that have emerged from the discussion on Twitter include the types of food labeling, food industry, healthy vs. unhealthy foods in the context of food labeling, EU regulation, political conflicts, and science and education. Nutri-Score dominated the discussion on Twitter. General topics were perceived negatively by Twitter users with more positive sentiments toward the food industry, while negative sentiments were observed toward the discourse of political conflicts. The network analysis showed that a centralized communication was hardly existed between countries. Our results reveal that the discussion of FOPL on Twitter is limited to a very limited group of people, and it seems necessary to inform a wide range of consumers about existing and upcoming FOPL schemes. Educational programs should empower consumers to understand what a healthy diet is and how it relates to FOPL, regardless of the existing labeling system.