The Botization of Science? Large-scale study of the presence and impact of Twitter bots in science dissemination

Twitter bots are a controversial element of the platform, and their negative impact is well known. In the field of scientific communication, they have been perceived in a more positive light, and the accounts that serve as feeds alerting about scientific publications are quite common. However, despite being aware of the presence of bots in the dissemination of science, no large-scale estimations have been made nor has it been evaluated if they can truly interfere with altmetrics. Analyzing a dataset of 3,744,231 papers published between 2017 and 2021 and their associated 51,230,936 Twitter mentions, our goal was to determine the volume of publications mentioned by bots and whether they skew altmetrics indicators. Using the BotometerLite API, we categorized Twitter accounts based on their likelihood of being bots. The results showed that 11,073 accounts (0.23% of total users) exhibited automated behavior, contributing to 4.72% of all mentions. A significant bias was observed in the activity of bots. Their presence was particularly pronounced in disciplines such as Mathematics, Physics, and Space Sciences, with some specialties even exceeding 70% of the tweets. However, these are extreme cases, and the impact of this activity on altmetrics varies by speciality, with minimal influence in Arts & Humanities and Social Sciences. This research emphasizes the importance of distinguishing between specialties and disciplines when using Twitter as an altmetric

A scoping review on the use of natural language processing in research on political polarization: trends and research prospects

As part of the “text-as-data” movement, Natural Language Processing (NLP) provides a computational way to examine political polarization. We conducted a methodological scoping review of studies published since 2010 ( n = 154) to clarify how NLP research has conceptualized and measured political polarization, and to characterize the degree of integration of the two different research paradigms that meet in this research area. We identified biases toward US context (59%), Twitter data (43%) and machine learning approach (33%). Research covers different layers of the political public sphere (politicians, experts, media, or the lay public), however, very few studies involved more than one layer. Results indicate that only a few studies made use of domain knowledge and a high proportion of the studies were not interdisciplinary. Those studies that made efforts to interpret the results demonstrated that the characteristics of political texts depend not only on the political position of their authors, but also on other often-overlooked factors. Ignoring these factors may lead to overly optimistic performance measures. Also, spurious results may be obtained when causal relations are inferred from textual data. Our paper provides arguments for the integration of explanatory and predictive modeling paradigms, and for a more interdisciplinary approach to polarization research

Social media mining under the COVID-19 context: Progress, challenges, and opportunities

Social media platforms allow users worldwide to create and share information, forging vast sensing networks that allow information on certain topics to be collected, stored, mined, and analyzed in a rapid manner. During the COVID-19 pandemic, extensive social media mining efforts have been undertaken to tackle COVID-19 challenges from various perspectives. This review summarizes the progress of social media data mining studies in the COVID-19 contexts and categorizes them into six major domains, including early warning and detection, human mobility monitoring, communication and information conveying, public attitudes and emotions, infodemic and misinformation, and hatred and violence. We further document essential features of publicly available COVID-19 related social media data archives that will benefit research communities in conducting replicable and repro�ducible studies. In addition, we discuss seven challenges in social media analytics associated with their potential impacts on derived COVID-19 findings, followed by our visions for the possible paths forward in regard to social media-based COVID-19 investigations. This review serves as a valuable reference that recaps social media mining efforts in COVID-19 related studies and provides future directions along which the information harnessed from social media can be used to address public health emergencies

Conceptualising the panic buying phenomenon during COVID-19 as an affective assemblage

Purpose-This study aims to conceptualise the panic buying behaviour of consumers in the UK during the novel COVID-19 crisis, using the assemblage approach as it is non-deterministic and relational and affords new ways of understanding the phenomenon. Design/methodology/approach-The study undertakes a digital ethnography approach and content analysis of Twitter data. A total of 6,803 valid tweets were collected over the perio

Social media mental health analysis framework through applied computational approaches

Studies have shown that mental illness burdens not only public health and productivity but also established market economies throughout the world. However, mental disorders are difficult to diagnose and monitor through traditional methods, which heavily rely on interviews, questionnaires and surveys, resulting in high under-diagnosis and under-treatment rates. The increasing use of online social media, such as Facebook and Twitter, is now a common part of people’s everyday life. The continuous and real-time user-generated content often reflects feelings, opinions, social status and behaviours of individuals, creating an unprecedented wealth of person-specific information. With advances in data science, social media has already been increasingly employed in population health monitoring and more recently mental health applications to understand mental disorders as well as to develop online screening and intervention tools. However, existing research efforts are still in their infancy, primarily aimed at highlighting the potential of employing social media in mental health research. The majority of work is developed on ad hoc datasets and lacks a systematic research pipeline. [Continues.]</div

Data mining Twitter for cancer, diabetes, and asthma insights

Twitter may be a data resource to support healthcare research. Literature is still limited related to the potential of Twitter data as it relates to healthcare. The purpose of this study was to contrast the processes by which a large collection of unstructured disease-related tweets could be converted into structured data to be further analyzed. This was done with the objective of gaining insights into the content and behavioral patterns associated with disease-specific communications on Twitter. Twelve months of Twitter data related to cancer, diabetes, and asthma were collected to form a baseline dataset containing over 34 million tweets. As Twitter data in its raw form would have been difficult to manage, three separate data reduction methods were contrasted to identify a method to generate analysis files, maximizing classification precision and data retention. Each of the disease files were then run through a CHAID (chi-square automatic interaction detector) analysis to demonstrate how user behavior insights vary by disease. Chi-square Automatic Interaction Detector (CHAID) was a technique created by Gordon V. Kass in 1980. CHAID is a tool used to discover the relationship between variables. This study followed the standard CRISP-DM data mining approach and demonstrates how the practice of mining Twitter data fits into this six-stage iterative framework. The study produced insights that provide a new lens into the potential Twitter data has as a valuable healthcare data source as well as the nuances involved in working with the data

Applications of social data science to environmental communication on social media

Effective environmental communication and activism are critical to the development of public support for the policies, ideological shifts, and behavioural changes needed to systemically address climate change. The last five years have seen a significant increase in global environmental activism on social media, yet there are still many gaps in our understanding of the strategies used, their effectiveness, and the challenges they face in supporting the pro-environmental movement. This thesis presents a combination of interdisciplinary case studies examining three under-studied contexts of environmental communication and activism on social media. Together, they advance our understanding of important dynamics impacting the effectiveness of online activism and provide motivation for key methodological shifts in the way scholars approach researching this topic. The thesis begins with general motivation and contextualisation of the work in Chapter 1. Then, I provide an overview of the research which has analysed environmental communication and activism on social media so far and identify key blind spots in Chapter 2. I then present four empirical chapters, each addressing one of these gaps using mixed-method and interdisciplinary computational social science approaches. The first empirical chapter (Chapter 3) addresses commercial engagement and controversy in vegan activism as a site of targeted environmental activism. The second empirical chapter (Chapter 4) investigates the relationship between psycholinguistic framing in posts of environmental activists and audience engagement on Twitter. The third empirical chapter (Chapter 5) presents an experiment in which the correlational findings of the previous chapter, and a potential cognitive mechanism driving them, are investigated offline. The fourth and final empirical chapter (Chapter 6) compares the topic and sentiment framing used to discuss COP26 conference outcomes in a sample of English-language mainstream (Australia, India, UK, and US) and social media (Facebook and Instagram), paying particular attention to how different stakeholders (major news outlets, politicians, activists, and NGOs) overlap and diverge in their commentary and discussing what implications this has for the development of coherent public dialogue on environmental policy moving forward. To wrap up the thesis, the Conclusion (Chapter 7) summarises the empirical work and individual contributions of each chapter. It also discusses contributions of the thesis as a whole, bringing the findings of each study into conversation with one another to motivate the importance for future research to 1) take increasingly interdisciplinary approaches to the study of environmental activism and evaluations of its effectiveness, and 2) move away from social media-only studies and connect social media dynamics to micro- and macro-level offline outcomes