Microblogging Platform Twitter Research Articles

'Laughing at us'

That Donald J. Trump won the US Presidential Election of 2016 defied the expectations of almost all seasoned observers of the US political system. Scholarly explanations stress structural factors that produced a substantial cohort of identity-vulnerable voters in tandem with Trump’s personal populist appeal. Trump benefitted from his political outsider status, celebrity familiarity, heteronormative masculinity, and unconventional rhetorical style including humour, all of which were amplified (and also mocked) by extensive mainstream news media coverage. Trump’s distinctive political use of the social media micro-blogging platform Twitter (now known as X) in engaging those cohorts has also been emphasized. The research presented here sits at the confluence of prior scholarly work on structural causes of identity vulnerability, on contestation involving humour, and on the enhanced political usage of Twitter. It examines the historical record of Trump’s tweets and re-tweets to see if and how the demarcation of his ‘humour brand’ affectively engaged his substantial Twitter followers and may thereby have contributed to his electoral success in 2016.

Reluctant heroes: New doctors negotiating their identities dialogically on social media.

Ensuring that students transition smoothly into the identity of a doctor is a perpetual challenge for medical curricula. Developing professional identity, according to cultural-historical activity theory, requires negotiation of dialectic tensions between individual agency and the structuring influence of institutions. We posed the research question: How do medical interns, other clinicians and institutions dialogically construct their interacting identities? Our qualitative methodology was rooted in dialogism, Bakhtin's cultural-historical theory that accounts for how language mediates learning and identity. Reasoning that the COVID pandemic would accentuate and expose pre-existing tensions, we monitored feeds into the Twitter microblogging platform during medical students' accelerated entry to practice; identified relevant posts from graduating students, other clinicians and institutional representatives; and kept an audit trail of chains of dialogue. Sullivan's dialogic methodology and Gee's heuristics guided a reflexive, linguistic analysis. There was a gradient of power and affect. Institutional representatives used metaphors of heroism to celebrate 'their graduates', implicitly according a heroic identity to themselves as well. Interns, meanwhile, identified themselves as incapable, vulnerable and fearful because the institutions from which they had graduated had not taught them to practise. Senior doctors' posts were ambivalent: Some identified with institutions, maintaining hierarchical distance between themselves and interns; others, along with residents, acknowledged interns' distress, expressing empathy, support and encouragement, which constructed an identity of collegial solidarity. The dialogue exposed hierarchical distance between institutions and the graduates they educated, which constructed mutually contradictory identities. Powerful institutions strengthened their identities by projecting positive affects onto interns who, by contrast, had fragile identities and sometimes strongly negative affects. We speculate that this polarisation may be contributing to the poor morale of doctors in training and propose that, to maintain the vitality of medical education, institutions should seek to reconcile their projected identities with the lived identities of graduates.

From resolution to resecuritization: populist communication of the AKP’s Kurdish peace process in Turkey

AbstractThis article contributes to the existing literature on the populist online communication of governments. We look at the role of the micro-blogging social media platform Twitter under the authoritarian rule of President Recep Tayyip Erdoğan and the wider Justice and Development Party (Adalet ve Kalkınma Partisi; AKP) during the peace process. We carried out a rhetorical analysis of the Twitter posts of four key AKP actors – Recep Tayyip Erdoğan, Ahmet Davutoğlu, Yalçın Akdoğan, and Efkan Ala – between July 1, 2012 and November 1, 2015. First, we show that the AKP actors persistently label the Kurdish political movement in Turkey and in Syria as a threat to the national security of Turkey, reflected in their rhetoric toward the remilitarization and resecuritization of Turkey’s Kurdish question within and across its borders. Second, we argue that the AKP used the peace process and various persuasive communicative techniques not only to consolidate Kurdish electoral support, but also to reach its aim to remove the Kemalist military–bureaucratic tutelage in Turkey that was replaced with hyper-presidentialism under the strong personality cult of Erdoğan. Third, we argue that Erdoğan’s increased one-man power has been reflected in the AKP’s branding itself as the only viable choice for the Kurdish region’s stability, which has blocked more constructive dialogue toward a peaceful resolution to the Kurdish question.

Sentimental analysis of female entrepreneurs based on public tweets

After the declaration of the COVID 19 pandemic, jobs and occupations had to return to normal, however many people lost their main source of resources, so the incorporation of women in different ventures has been a flag to get ahead with work for their families, so the objective of this work is to identify opinions about entrepreneurs, using the technique of sentimental analysis to classify unstructured information from public tweets using for data processing, the Rstudio software with the retweet, word cloud and emo libraries collecting the feelings that people have through the microblogging platform Twitter in the period from June 20 to July 10, 2022. The results show that 74.53% of users perceive positive and very positive feelings toward women entrepreneurs, so it can be concluded that the impact of the incorporation of women entrepreneurs, which although at this time is still uncertain is perceived as an important factor for the economic recovery.

«Каждое мое утро начиналось с Пипса и кофе. Не представляю себе, как я теперь буду…»: цифровое чтение личного дневника XVII в.

This article deals with the issue of digital reading of personal diaries based on the analysis of two projects by Phil Gyford involving the publication of Samuel Pepys’ diary — on a site specifically dedicated to the diary and on the microblogging platform Twitter. When discussing the genre of the personal diary and its publication, generally the problem of privacy and its violation comes to the forefront: it is seen as central to understanding the impressions as well as the experiences of a reader “immersed” in someone else’s private life and experiencing both a feeling of guilt, opposing him/herself to the author/hero, and nostalgia for missed opportunities, which brings closer to the text the view that the diarist himself might have, upon re-reading his/her notes. Consideration of the forms of diary publication on the Internet, which sought to create “a living reading” and assumed a reading model fundamentally different from that of a printed book, allows us to pay attention to other aspects of the reader’s perception of someone else’s personal diary, conditioned by the specifics of the media and everyday practices. Analysis of the image of Pepys himself, of his text, and of the past in general as they appear in the comments of the users of both projects touches upon three main aspects — the influence of media on the perception of the narrative, the framing of the text being read, and the inclusion of the diary in the reader’s everyday life. The comparison of the two projects, which reveals, in full correspondence with Marshall McLuhan’s idea of “the medium is the message”, a significant difference in the perception of the same text, problematizes the already criticized genre definition of the diary and its characteristics. Users’ comments allow us to speak not only about other mechanisms of forming a sense of privacy and closeness to the author, which are considered to be among the most important for defining the private diary genre, but also regarding the issue of privacy: in certain practices of reading personal diaries the angle of “peeping” can change into its opposite — the diary’s interference in the private life of the reader.

1000 Days of COVID-19: A Gender-Based Long-Term Investigation into Attitudes With Regards to Vaccination

The coronavirus pandemic has undoubtedly been one of the major recent events that have affected our society at the global level. During this period, unprecedented measures have been imposed worldwide by authorities in an effort to contain the spread of the disease. These measures have led to a worldwide debate among the public, occurring not least within the forum of social media, tapping into pre-existing trends of skepticism, such as vaccine hesitancy. At the same time, it has become apparent that the pandemic affected women and men differently. With these two themes in view, the paper aims to analyze using a data-driven approach the evolution of opinions with regards to vaccination against COVID-19 throughout the entire duration of the pandemic from the point of view of gender. For this analysis, approximately 1,500,000 short user-contributed texts have been retrieved from the popular microblogging platform Twitter, posted between 30 January 2020 and 30 November 2022. Using a machine learning approach, several classifiers have been trained to identify the likely gender ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">female</i> or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">male</i> ) of the author, as well as the stance of the specific post towards the COVID-19 vaccines ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">neutral</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in favor</i> , or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">against</i> ), achieving 85.69% and 93.64% weighted accuracy measures for each problem, respectively. Based on this analysis, it can be observed that most tweets exhibit a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">neutral</i> stance, while the number of tweets <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in favor</i> of vaccination is greater than the number of tweets opposing vaccination, with the distribution varying across time in response to specific events. The subject matter of the tweets varied more between stances than between genders, suggesting that there is no significant difference between the contents of tweets posted by females and males. We also find that while the overall engagement on Twitter with the topic of vaccination against COVID-19 is on the wane, there has been a rise in the number of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">against</i> tweets continuing into the present.

Efficient and Reliable Geocoding of German Twitter Data to Enable Spatial Data Linkage to Official Statistics and Other Data Sources

More and more, social scientists are using (big) digital behavioral data for their research. In this context, the social network and microblogging platform Twitter is one of the most widely used data sources. In particular, geospatial analyses of Twitter data are proving to be fruitful for examining regional differences in user behavior and attitudes. However, ready-to-use spatial information in the form of GPS coordinates is only available for a tiny fraction of Twitter data, limiting research potential and making it difficult to link with data from other sources (e.g., official statistics and survey data) for regional analyses. We address this problem by using the free text locations provided by Twitter users in their profiles to determine the corresponding real-world locations. Since users can enter any text as a profile location, automated identification of geographic locations based on this information is highly complicated. With our method, we are able to assign over a quarter of the more than 866 million German tweets collected to real locations in Germany. This represents a vast improvement over the 0.18% of tweets in our corpus to which Twitter assigns geographic coordinates. Based on the geocoding results, we are not only able to determine a corresponding place for users with valid profile locations, but also the administrative level to which the place belongs. Enriching Twitter data with this information ensures that they can be directly linked to external data sources at different levels of aggregation. We show possible use cases for the fine-grained spatial data generated by our method and how it can be used to answer previously inaccessible research questions in the social sciences. We also provide a companion R package, nutscoder, to facilitate reuse of the geocoding method in this paper.

‘STFU and start listening to how scared we are’: Resisting misogyny on Twitter via #NotAllMen

This article focuses on the strategies that were used to resist misogyny on the microblogging platform Twitter during March 2021, a time when the hashtag #NotAllMen was trending. We take a critical feminist approach, combining corpus linguistics with a qualitative analysis of #NotAllMen users’ discursive strategies. This particular iteration of #NotAllMen followed the disappearance and subsequent rape and murder of Sarah Everard, a 33 year old white woman who was abducted from a street in London, UK, whilst walking home. Following a keyword analysis (Scott 1997) to survey a dataset of 18,701 tweets containing the hashtag #NotAllMen, we identify salient themes in a sub-set of keyword concordances, and produce a detailed qualitative analysis of the strategies deployed in ten randomly sampled tweets. Despite #NotAllMen initally being used as a statement of protest against supposedly unfair accusations levelled at ‘all’ men, our analysis illustrates the use of resistant and empowering strategies which challenge the misogyny of this message, re-framing the hashtag and thus acting as a form of resistance to its original message: that not all men enagage in gender-based violence. We argue that this points to the strategic use of social media to challenge harmful rhetoric, whereby users exploit the affordances of hashtags. Twitter users engaged in strategies including resistance, opposition, and polarity to the hashtag, evidenced through the linguistic use of expletives, insults, and direct address, most often emerging through metadiscussion of the #NotAllMen hashtag itself; this works as a form of collective counter-protest through hashtag reframing. The hashtag reframing operates as a tool to show how those using the hashtag to focus on the defence of men, rather than critiquing the sociocultural dominance of misogynistic behaviour, ignored the fear experienced by all women and girls of being victims of gender-based violence, rape and murder.

Business-centered versus socially responsible corporate diversity communication. An assessment of stakeholder (dis)agreement on Twitter

This study assessed the business-centered and CSR-centered diversity communication of five American corporations along with the ensuing responses on the microblogging platform Twitter, the present analysis contributes to the literature on online diversity communication and public relations. The results revealed that companies that had long-term consistency in approaching diversity as CSR received more agreeable responses than those that made swift changes in the aftermath of increased pressure from activist groups. In addition, companies that communicated to a greater degree about the impact of diversity on the bottom line engendered higher user agreement than those who discussed diversity primarily from a CSR perspective.The results shed light on the importance of message consistency throughout longer periods of time.

Quantifying the drivers behind collective attention in information ecosystems

Understanding human interactions in online communications is of paramount importance for our society. Alarming phenomena such as the spreading of fake news or the formation of echo-chambers can emerge in unhealthy communication environments and, ultimately, undermine the democratic discourse. In this context, unveiling the individual drivers that give rise to collective attention can help to conserve the health of our information ecosystems. Here, following a recently proposed analogy between natural and information ecosystems, we explore how competition for attention in online social networks and the strategies adopted by the users to maximize their visibility shape our communication dynamics. Specifically, by analyzing large-scale datasets from the micro-blogging platform Twitter and performing numerical modeling of the system dynamics, we are able to measure the amount of competition for attention experienced by users and how it changes when exogenous events captivate collective attention. The work relies on topic modeling to extract users’ interests and memes context from the data and a framework based on ecological niche theory to quantify the strength of negative (competitive) and positive (mutualistic) interactions for both users and memes. Interestingly, our findings show two different behaviors. While memes undergo a sharp increase in competition during exceptional events that can lead to their extinction, users perceive a decrease in effective competition due to a stronger effect of mutualistic interaction, explaining the focus of collective attention around specific topics. Finally, to confirm our results we reproduce the observed shifts with a data-driven model of species dynamics.

Understanding the Emotional Intelligence Discourse on Social Media: Insights from the Analysis of Twitter.

Social networks have created an information diffusion corpus that provides users with an environment where they can express their views, form a community, and discuss topics of similar or dissimilar interests. Even though there has been an increasingly rising demand for conducting an emotional analysis of the users on social media platforms, the field of emotional intelligence (EI) has been rather slow in exploiting the enormous potential that social media can play in the research and practice of the framework. This study, thus, tried to examine the role that the microblogging platform Twitter plays in enhancing the understanding of the EI community by building on the Twitter Analytics framework of Natural Language Processing to further develop the insights of EI research and practice. An analysis was conducted on 53,361 tweets extracted using the hashtag emotional intelligence through descriptive analytics (DA), content analytics (CA), and network analytics (NA). The findings indicated that emotional intelligence tweets are used mostly by speakers, psychologists (or other medical professionals), and business organizations, among others. They use it for information dissemination, communication with stakeholders, and hiring. These tweets carry strong positive sentiments and sparse connectedness. The findings present insights into the use of social media for understanding emotional intelligence.

Non-Communicable Diseases and Social Media: A Heart Disease Symptoms Application

Social media platforms have become ubiquitous and allow users to share information in real-time. Our study uses data analytics as an approach to explore non-communicable diseases on social media platforms and to identify trends and patterns of related disease symptoms. Exploring epidemiological patterns of non-communicable diseases is vital given that they have become prevalent in low-income communities, accounting for about 38 million deaths worldwide. We collected data related to multiple disease conditions from the Twitter microblogging platform and zoomed into symptoms related to heart diseases. As part of our analyses, we focussed on the mechanism and trends of disease occurrences. Our results show that specific symptoms may be attributed to multiple disease conditions and it is viable to identify trends and patterns of their occurrences using a structured analytics approach. This can then act as a supplementary tool to support epidemiological initiatives that monitor non-communicable diseases. Based on the study’s results, we identify that non-communicable disease surveillance approach using social media analytics could support the design of effective health intervention strategies.

Characterizing Deleted Tweets and Their Authors

This paper provides a detailed characterization of tweets posted and then later deleted by their authors, in the Twitter microblogging platform. Our characterization shows significant personality differences between users who delete their tweets and those who do not. We find that users who delete their tweets are more likely to be extroverted and neurotic while being less conscientious. Further, although deleted tweets contain more negative sentiment and swear words, they also show significant signs of being thoughtfully constructed.

Modeling Public Mood and Emotion: Twitter Sentiment and Socio-Economic Phenomena

We perform a sentiment analysis of all tweets published on the microblogging platform Twitter in the second half of 2008. We use a psychometric instrument to extract six mood states (tension, depression, anger, vigor, fatigue, confusion) from the aggregated Twitter content and compute a six-dimensional mood vector for each day in the timeline. We compare our results to a record of popular events gathered from media and sources. We find that events in the social, political, cultural and economic sphere do have a significant, immediate and highly specific effect on the various dimensions of public mood. We speculate that large scale analyses of mood can provide a solid platform to model collective emotive trends in terms of their predictive value with regards to existing social as well as economic indicators.

Partisanship over security: Public narratives via Twitter on foreign interferences in the 2016 and 2020 U.S. presidential elections

Using a mixed methods approach, we explore the main narrative themes conveyed by the public, via microblogging platform Twitter, in regard to foreign interferences in the U.S. presidential elections of 2016 and 2020. Our findings show that rather than expressing serious concerns about foreign threats to the United States’ democracy and its electoral process, the tweets reflected the political polarization that has come to characterize the American public. Rather than perceiving the interferences as a national security issue, the public appeared to selectively use the foreign threats to bolster their partisan positions.

German Political Journalists and the Normalization of Twitter

ABSTRACT Since its introduction in 2006, journalists found ways to incorporate the microblogging platform Twitter into their daily work routines. Communication scholars quickly viewed the journalistic adoption of Twitter as a normalization process where journalists adapt a new media affordance in line with their professional role conceptions, norms, and work practices. Rather recently, concerns regarding the applicability of the normalization concept were expressed. In hindsight, the literature on normalization must be updated to draw on results from international contexts. The normalization hypothesis also benefits from theoretical and operational additions to highlight its continued usefulness in times when journalists have come to be acquainted with Twitter. The present study does so by providing a comprehensive analysis of how political journalists from Germany tweet while also reframing the operationalization of the concept by comparing journalists’ tweets with temporally corresponding articles published in their respective media outlets. Quantitative data from a week-long sampling period in late June and early July 2018, as well as semi-structured interviews, reveal that the tweets of German political journalists and the articles published across the media outlets they work for correspond in the choice of content and are largely consistent with regard to the interpretive standpoints chosen.

Assessing vulnerability to psychological distress during the COVID-19 pandemic through the analysis of microblogging content

In recent years we have witnessed a growing interest in the analysis of social media data under different perspectives, since these online platforms have become the preferred tool for generating and sharing content across different users organized into virtual communities, based on their common interests, needs, and perceptions. In the current study, by considering a collection of social textual contents related to COVID-19 gathered on the Twitter microblogging platform in the period between August and December 2020, we aimed at evaluating the possible effects of some critical factors related to the pandemic on the mental well-being of the population. In particular, we aimed at investigating potential lexicon identifiers of vulnerability to psychological distress in digital social interactions with respect to distinct COVID-related scenarios, which could be “at risk” from a psychological discomfort point of view. Such scenarios have been associated with peculiar topics discussed on Twitter. For this purpose, two approaches based on a “top-down” and a “bottom-up” strategy were adopted. In the top-down approach, three potential scenarios were initially selected by medical experts, and associated with topics extracted from the Twitter dataset in a hybrid unsupervised-supervised way. On the other hand, in the bottom-up approach, three topics were extracted in a totally unsupervised way capitalizing on a Twitter dataset filtered according to the presence of keywords related to vulnerability to psychological distress, and associated with at-risk scenarios. The identification of such scenarios with both approaches made it possible to capture and analyze the potential psychological vulnerability in critical situations.

Evolution of the political opinion landscape during electoral periods

We present a study of the evolution of the political landscape during the 2015 and 2019 presidential elections in Argentina, based on data obtained from the micro-blogging platform Twitter. We build a semantic network based on the hashtags used by all the users following at least one of the main candidates. With this network we can detect the topics that are discussed in the society. At a difference with most studies of opinion on social media, we do not choose the topics a priori, they emerge from the community structure of the semantic network instead. We assign to each user a dynamical topic vector which measures the evolution of her/his opinion in this space and allows us to monitor the similarities and differences among groups of supporters of different candidates. Our results show that the method is able to detect the dynamics of formation of opinion on different topics and, in particular, it can capture the reshaping of the political opinion landscape which has led to the inversion of result between the two rounds of 2015 election.

Social Sensing of Heatwaves.

Heatwaves cause thousands of deaths every year, yet the social impacts of heat are poorly measured. Temperature alone is not sufficient to measure impacts and “heatwaves” are defined differently in different cities/countries. This study used data from the microblogging platform Twitter to detect different scales of response and varying attitudes to heatwaves within the United Kingdom (UK), the United States of America (US) and Australia. At the country scale, the volume of heat-related Twitter activity increased exponentially as temperature increased. The initial social reaction differed between countries, with a larger response to heatwaves elicited from the UK than from Australia, despite the comparatively milder conditions in the UK. Language analysis reveals that the UK user population typically responds with concern for individual wellbeing and discomfort, whereas Australian and US users typically focus on the environmental consequences. At the city scale, differing responses are seen in London, Sydney and New York on governmentally defined heatwave days; sentiment changes predictably in London and New York over a 24-h period, while sentiment is more constant in Sydney. This study shows that social media data can provide robust observations of public response to heat, suggesting that social sensing of heatwaves might be useful for preparedness and mitigation.

A Social Media Campaign (#datasaveslives) to Promote the Benefits of Using Health Data for Research Purposes: Mixed Methods Analysis.

BackgroundSocial media provides the potential to engage a wide audience about scientific research, including the public. However, little empirical research exists to guide health scientists regarding what works and how to optimize impact. We examined the social media campaign #datasaveslives established in 2014 to highlight positive examples of the use and reuse of health data in research.ObjectiveThis study aims to examine how the #datasaveslives hashtag was used on social media, how often, and by whom; thus, we aim to provide insights into the impact of a major social media campaign in the UK health informatics research community and further afield.MethodsWe analyzed all publicly available posts (tweets) that included the hashtag #datasaveslives (N=13,895) on the microblogging platform Twitter between September 1, 2016, and August 31, 2017. Using a combination of qualitative and quantitative analyses, we determined the frequency and purpose of tweets. Social network analysis was used to analyze and visualize tweet sharing (retweet) networks among hashtag users.ResultsOverall, we found 4175 original posts and 9720 retweets featuring #datasaveslives by 3649 unique Twitter users. In total, 66.01% (2756/4175) of the original posts were retweeted at least once. Higher frequencies of tweets were observed during the weeks of prominent policy publications, popular conferences, and public engagement events. Cluster analysis based on retweet relationships revealed an interconnected series of groups of #datasaveslives users in academia, health services and policy, and charities and patient networks. Thematic analysis of tweets showed that #datasaveslives was used for a broader range of purposes than indexing information, including event reporting, encouraging participation and action, and showing personal support for data sharing.ConclusionsThis study shows that a hashtag-based social media campaign was effective in encouraging a wide audience of stakeholders to disseminate positive examples of health research. Furthermore, the findings suggest that the campaign supported community building and bridging practices within and between the interdisciplinary sectors related to the field of health data science and encouraged individuals to demonstrate personal support for sharing health data.