Twitter Activity Research Articles

Anti-vaccination attitude trends during the COVID-19 pandemic: A machine learning-based analysis of tweets.

Vaccine hesitancy has been ranked by the World Health Organization among the top 10 threats to global health. With a surge in misinformation and conspiracy theories against vaccination observed during the COVID-19 pandemic, attitudes toward vaccination may be worsening. This study investigates trends in anti-vaccination attitudes during the COVID-19 pandemic and within the United States, Canada, the United Kingdom, and Australia. Vaccine-related English tweets published between 1 January 2020 and 27 June 2021 were used. A deep learning model using a dynamic word embedding method, Bidirectional Encoder Representations from Transformers (BERTs), was developed to identify anti-vaccination tweets. The classifier achieved a micro F1 score of 0.92. Time series plots and country maps were used to examine vaccination attitudes globally and within countries. Among 9,352,509 tweets, 232,975 (2.49%) were identified as anti-vaccination tweets. The overall number of vaccine-related tweets increased sharply after the implementation of the first vaccination round since November 2020 (daily average of 6967 before vs. 31,757 tweets after 9/11/2020). The number of anti-vaccination tweets increased after conspiracy theories spread on social media. Percentages of anti-vaccination tweets were 3.45%, 2.74%, 2.46%, and 1.86% for the United States, the United Kingdom, Australia, and Canada, respectively. Strategies and information campaigns targeting vaccination misinformation may need to be specifically designed for regions with the highest anti-vaccination Twitter activity and when new vaccination campaigns are initiated.

Uncovering Factors Influencing Millennials’ Use of Non-Standard Words in Twitter

Social media communication has its own language features and one aspect is modified spelling of standard words. Social media users use shortened words with full awareness of the meanings, and new non-standard words are constantly added to the repertoire of social media language. A pertinent question is whether social media users learn these non-standard words to use or whether they also contribute to the vocabulary used in social media communication. The study examined Malaysian millennials’ use of non-standard words in Twitter and their reasons for shortening words. For the non-standard words, data were collected from 200 active Twitter users whereas data on reasons for shortening words were collected from 30 users. The results showed that the Malaysian millennials frequently used non-standard spelling of words. The three top words were “ni” (this), “nak” (want), and X (negation). The main reasons for the Twitter users to shorten words were the 280-character limit per tweet, user convenience, and characteristics of words. The Twitter users felt free to create new spellings of standard words at times for fun, but most of the time, they use the common non-standard words. The Malaysian millennials reported that they were inclined to shorten long and complex words, and words with many vowels. The study suggests that Twitter users balance between speed in communication and preservation of meaning when using non-standard words. Keywords: Social media, Twitter, non-standard words, word formation processes, shortening of words.

Sustainability in luxury: insights from Twitter activities

ABSTRACT Sustainability presents one of the most pressing challenges across different industries, especially for luxury brands. This study reveals the role of sustainability in luxury consumption by collecting and analyzing data from over half a million Tweets with 762 sustainability luxury consumption hashtags. Sustainability’s importance in online discussion is steadily increasing. Using balance theory, we combined sentiment and text mining to decrypt and reveal online audience’s perceptions of sustainable luxury. The findings highlight what online public considers sustainable, the importance of these perceptions for specific sectors of the luxury industry, and which emotions sustainable luxury perceptions trigger. We reveal online audience’s sentiments in the form of opinions, evaluations, and emotions toward sustainable luxury. This research serves as a critical exploration of the much-needed alignment between luxury and sustainability. This work aids not only scholars but also luxury brand managers in their aim to comprehend and keep pace with the increasing importance of sustainability in the luxury sector.

ANALISIS JARINGAN KOMUNIKASI OPINI PUBLIK PADA FENOMENA “PEMECATAN Dr TERAWAN” DI TWITTER

This study aims to determine the system-level communication network and actors on the “Terawan” link and to find out public opinion about this phenomenon and how the IDI organization reacts in responding to public opinion. By using the theory of Computer Mediated Communication (CMC). The method used in this research is a quantitative approach with the method of Social Network Analysis with Netlytic and Gephi and content analysis. The population used in this study are active Twitter users, with a maximum sample of 2500 obtained from Netlytic. The results showed that this network has 1969 Nodes and 1991 Edges, with 5 large clusters in this network. This network has a value of Diameter 19, Destiny 0.000604, Reciprocity 0.011160, Centralization 0.107200 and Modularity 0.810800. The most influential actor in this network is @heraloebs with a Degree Centrality value of 391 and an Eigenvector Centrality value of 1.0. Therefore, this network includes a network that is widespread in the distribution of information, with influential actors in the distribution of information such as @heraloebss. While the focus of communication carried out by the IDI organization in responding to public opinion regarding the phenomenon of “Dr Terawan’s dismissal”

An exploratory study of social media's role in facilitating public participation in e‐rulemaking using computational text analysis tools

AbstractGovernment agencies struggle to use social media effectively to engage and involve the public. However, we know little about how individuals and groups on the other side of these efforts actually use social media to participate in government decision‐making processes, independent of government‐led engagement efforts. This exploratory study aims to address this by looking closer at how Twitter users self‐mobilize after a particular event in which they may be motivated to participate. Specifically, the study examines Twitter activity before and after the United States Department of the Interior proposed changes to its Freedom of Information Act (FOIA) regulations that would limit public access to agency records. Acomputational text analysis of 5000 tweets using #FOIA over a 99‐day period showed significant changes in the sentiment of the dialog before and after the proposed rule was announced. Furthermore, structural topic models show evidence of a diverse set of stakeholders using Twitter to exercise voice and share information about the proposed rule. Findings suggest that citizens and interest groups are taking to Twitter to mobilize participation in the rulemaking process prompting the need for more research on how the public uses social media to influence government decision‐making, absent of government‐led engagement efforts.

Doing Twitter, Postdevelopmental Pedagogies, and Digital Activism

In this article, we interrogate how we might manifest early childhood education’s Twitter purview as a space for thinking with postdevelopmental pedagogies. Accordingly, we pay attention to the ethics and politics that shape our Twitter practices, asking how these activate postdevelopmental provocations. In this sense, postdevelopmental pedagogies refer to processes and questions that interrupt the assumptions, objectivity, universalism, and technocratic instrumentalism of child development that so often pervade ECE practice, including much of the #earlychildhoodeducation content. Anchored in the two Twitter accounts that we coordinate, we outline four practices for doing Twitter with postdevelopmental provocations: counterpublics, counter-narratives, and counter-memory, collectivity, and digital feminist activism. We then work through two examples, showing how we draw these practices into our decision making as we craft tweets to activate postdevelopmental questions. We conclude by offering forward questions that educators, pedagogists, researchers, and activists might carry into their own Twitter practices. Keywords: early childhood education, Twitter, postdevelopmental pedagogies, digital activism

Pengungkapan Diri Mahasiswa pada Media Sosial Twitter (Studi Etnografi Virtual Akun Autobase @Collegemenfess)

The phenomenon of anonymous self-disclosure on social media is currently increasingly found, one of which is on social media Twitter. The @collegemenfess Twitter account is an autobase account that is widely used as a medium for student self-disclosure, especially about college issues and campus life.The purpose of this study was to determine the form of areas and motives of self-disclosure and cyber culture formed on the autobase @collegemenfess account. The study used descriptive qualitative methods. The Data in this study were obtained through interviews with 5 informants and observations on communication activities in cyberspace Twitter account @collegemenfess. The results showed that there were two forms of johari Window area that occurred in the self-disclosure in the @collegemenfess account, which occurred in the open area and the blind area. Furthermore, it is known that there are five motives for users to do self-disclosure, the @collegemenfess account is anonymous, users are afraid to tell stories with others directly, to share information and experiences, to overcome problems and get a better understanding, and motivation and actualization. The study also found five levels of cyber culture formed, namely the first level of media space, where the @collegemenfess account uses Twitter as a medium. Second, the level of media documents, where the media documents in this account are the access, interaction, activities and responses from users. Third, the media object level, where the @collegemenfess account is used as a place to discuss such as exchanging stories and expressing yourself. Finally, the experience level, which is the reason the account owner created this account to provide a communication platform for students and user communication culture using an anonymous identity.

A New Artificial Intelligence-Based Hierarchical K-Means Clustering Technique to Detect Addictive Twitter Activity

To stop the COVID-19 epidemic from spreading among their populations, several countries have implemented lockdowns. Students are being forced to stay at home during these lockdowns, which is causing them to use mobile phones, social media, and other digital technologies more frequently than ever. Their poor utilization of these digital tools may be detrimental to their emotional and mental health. In this study, we implement an Artificial Intelligence (AI) approach named Hierarchy-based K-Means Clustering (HKMC) algorithm to group individuals with comparable Twitter consumption habits to detect addictive Twitter activity during the epidemic. The effectiveness of the suggested HKMC is evaluated in terms of accuracy, precision, recall, and f1-score in respect to the association between students’ mental health and mobile phone dependency. Additionally, this study offers a comparative examination of both the suggested and existing procedures.

The Twitter factor: How does Twitter impact #Stroke journals and citation rates?

Twitter is a social media platform popularly used by health practitioners, a trend that has been followed by medical journals. The impact of Twitter in bibliometrics of stroke-related literature is yet to be determined. We aimed to qualitatively assess the usage of Twitter by stroke journals and study the relationship between Twitter activity and citation rates of stroke articles. We used Journal Citation Reports to identify stroke journals. We collected the 2021 Impact Factor (IF) and the top 50 articles contributing to each journal IF. Relevant metrics were collected through Twitonomy, Altmetric, and Web of Science. The association between Twitter activity and citation rates was tested by a negative binomial regression model adjusted to journal's IF. A bivariate correlation and a log-linear regression model adjusted to journal's IF tested the relationship between number of tweets, tweeters, and the number of citations. We collected 450 articles across nine stroke-dedicated journals, five of which had a Twitter account. Only 95 (21%) articles had no Twitter mentions. The median number of citations in articles with versus without Twitter activity was 19 (10-39) versus 11(7-17) (P < 0.001). Twitter activity was associated with higher citation rates controlling for the IF (odds ratio (OR): 2.7, 95% confidence interval (CI) 2.12-3.38, P < 0.001). We found number of tweets to be predicted by the number of citations controlling for the IF (B = 0.33, 95% CI 0.29-0.40, β = 0.54, P < 0.001). Tweeted stroke articles tend to have higher citation rates which can be predicted by the number of tweets.

The public‐facing policy agenda of state legislatures: The communication of public policy via twitter

AbstractHow political actors choose which politics to focus on helps shape the outcome of the policy process. While the policy agenda of the federal government has received widespread attention, there is much less known about the policy agendas of the U.S. states. In this paper, we describe how and why states choose to have similar agendas. We rely on the Twitter activity of every state legislator in America to measure the attention that states pay to the categories developed in the Policy Agenda Project (PAP). We develop machine learning tools to measure the proportion of tweets from every state legislature from 2017 in each of the PAP policy topics. Our results show that states that the public‐facing policy agenda of a state legislature is correlated with the level of legislative professionalism and the partisan and ideological politics of the state. These results further our understanding of state policymaking and agenda setting.

How Elon Musk's Twitter activity moves cryptocurrency markets

Elon Musk, one of the richest individuals in the world, is considered a technological visionary and has a social network of over 110 million followers on social media platform Twitter. He regularly uses his social media presence to communicate on various topics, one of which is cryptocurrency, such as Bitcoin or Dogecoin. Using an event study approach, we analyze to what extent Musk's Twitter activity affects short-term cryptocurrency returns and volume. In other words, we investigate whether cryptocurrency markets exhibit a “Musk Effect”. Based on a sample of 47 cryptocurrency-related Twitter events, we identify significant positive abnormal returns and trading volume following such events. However, we discover that on average, price effects are only significant for Dogecoin-related Tweets but not for Bitcoin. This is because regarding the latter, the significant price effects of positive and negative news cancel each other out. Considered in isolation, non-negative tweets from Musk lead to significantly positive abnormal Bitcoin returns. Individual tweets do raise the price of Bitcoin by 16.9 % or reduce it by almost 11.8 %. Our study shows the significant impact that the social media activity of influential individuals can have on cryptocurrencies. This suggests a conflict between morals, risks of market manipulation and investor protection.

Understanding researchers’ Twitter uptake, activity and popularity—an analysis of applied research in Germany

Social media is opening up new avenues for disseminating research outputs. While prior literature points to the essential role of Twitter in this context, evidence on what determines variation in researchers´ Twitter engagement remains scarce. In this account-level study of Twitter usage, we consider how research productivity, research quality, and participation in academic conferences relate to Twitter uptake, activity and popularity, while also taking into account differences between academic disciplines. We use a population sample comprising data on Twitter engagement of researchers employed at the Fraunhofer-Gesellschaft, Europe´s largest applied research organization. We find that participation in academic conferences is strongly associated with Twitter uptake and popularity, but not with Twitter activity as such. We also find positive associations between research productivity and Twitter uptake as well as between research quality and popularity. Moreover, physicists use Twitter more than researchers from other disciplines, female researchers use Twitter less, and scientific age is negatively associated with Twitter activity. Our findings contribute to the literature on academic social media usage by providing indications for both push and pull mechanisms at play within social media research dissemination.

Hiding opinions from machine learning.

Recent breakthroughs in machine learning and big data analysis are allowing our online activities to be scrutinized at an unprecedented scale, and our private information to be inferred without our consent or knowledge. Here, we focus on algorithms designed to infer the opinions of Twitter users toward a growing number of topics, and consider the possibility of modifying the profiles of these users in the hope of hiding their opinions from such algorithms. We ran a survey to understand the extent of this privacy threat, and found evidence suggesting that a significant proportion of Twitter users wish to avoid revealing at least some of their opinions about social, political, and religious issues. Moreover, our participants were unable to reliably identify the Twitter activities that reveal one's opinion to such algorithms. Given these findings, we consider the possibility of fighting AI with AI, i.e., instead of relying on human intuition, people may have a better chance at hiding their opinion if they modify their Twitter profiles following advice from an automated assistant. We propose a heuristic that identifies which Twitter accounts the users should follow or mention in their tweets, and show that such a heuristic can effectively hide the user's opinions. Altogether, our study highlights the risk associated with developing machine learning algorithms that analyze people's profiles, and demonstrates the potential to develop countermeasures that preserve the basic right of choosing which of our opinions to share with the world.

Twitter Usage About Stuttering

Purpose: The objective of this study was to examine specific patterns of Twitter usage using common reference to stuttering. Method: The study used cross-sectional analysis of data generated from Twitter data. Twitter content, language, reach, users, accounts, temporal trends, and social networks were examined. Results: We identified and analyzed around 270,000 tweets from May to September 2018. Of the 100 most active Twitter accounts, individuals owned 64% compared with 31% owned by organizations. The individual accounts mainly consisted of comedians who stutter and individual's accounts. Of the individual accounts, 61% belonged to people who did not stutter or significant others of persons who stutter (PWSs). PWSs or significant others of PWSs accounted for 10.9% and 28.1%, respectively. Three unique tweets were identified as each having a reach of over 8 million Twitter users, with the greatest reach exceeding 40 million users. Temporal analysis identified marked retweet outliers (&gt; 2,800 and &gt; 2,200 retweets per day) that corresponded with a prank call made to the president on air force one by Stuttering John. Results indicate that, during data collection, Twitter is a platform that primarily functions to assail political opponents or denigrate adversaries, which further enhances the negative stereotypes about stuttering. Conclusions: The conclusions are based on the 100 most active/highest reach Twitter accounts. Twitter accounts owned by individuals outnumbered organization accounts, and people who do not stutter were the most frequently active account type. This study offers important insights regarding existing trends and offers an informed direction for future advocacy for PWSs and their supporting community via Twitter.

Gender-related and geographic trends in interactions between radiotherapy professionals on Twitter.

Twitter presence in academia has been linked to greater research impact which influences career progression. The purpose of this study was to analyse Twitter activity of the radiotherapy community around ESTRO congresses with a focus on gender-related and geographic trends. Tweets, re-tweets and replies, here designated as interactions, around the ESTRO congresses held in 2012-2021 were collected. Twitter activity was analysed temporally and, for the period 2016-2021, the geographical span of the ESTRO Twitter network was studied. Tweets and Twitter users collated during the 10years analysed were ranked based on number of 'likes', 're-tweets' and followers, considered as indicators of leadership/influence. Gender representation was assessed for the top-end percentiles. Twitter activity around ESTRO congresses was multiplied by 60 in 6years growing from 150 interactions in 2012 to a peak of 9097 in 2018. In 2020, during the SARS-CoV-2 pandemic, activity dropped by 60% to reach 2945 interactions and recovered to half the pre-pandemic level in 2021. Europe, North America and Oceania were strongly connected and remained the main contributors. While overall, 58% of accounts were owned by men, this proportion increased towards top liked/re-tweeted tweets and most-followed profiles to reach up to 84% in the top-percentiles. During the SARS-CoV-2 pandemic, Twitter activity around ESTRO congresses substantially decreased. Men were over-represented on the platform and in most popular tweets and influential accounts. Given the increasing importance of social media presence in academia the gender-based biases observed may help in understanding the gender gap in career progression.

What Does Your Bio Say? Inferring Twitter Users’ Depression Status From Multimodal Profile Information Using Deep Learning

People suffering from stress and various mental health problems find it easier to express and share their feelings on online platforms, such as Twitter. However, the imposed character limit (280 characters) by Twitter and infrequent online activities of a section of users poses a serious setback in using computational methods for mental health analysis or emotion research. Twitter provides rich metadata information about its users (such as user’s description, geolocation, and profile image URL), which can provide valuable information regarding the mental state of the users. We hypothesize that Twitter’s rich metadata information about their users can provide some valuable depression cues, which may help in an early low-profile evaluation. In this article, we investigate this hypothesis by developing an end-to-end multimodal multitask (MT) system for depression detection (primary task) and emotion recognition (auxiliary task), where the variation of emotion information based on different user descriptions assists the learning of the primary task. The proposed system attains 70% accuracy on the depression detection task outperforming several single-task (ST) baselines built on the various combination of input features. Our findings indicate that Twitters’s rich metadata information can be leveraged to detect depression among users with significant confidence.

The Utility of Social Media on Urology Residency Doximity Rankings.

Background Social media has been more widely used by urology residency programs since the COVID-19 pandemic. There are no studies on the relationship between Doximity residency ranking and social media usage in urology. Objectives In this study, we aim to investigate the relationship between social media usage and the academic prestige of urology residency programs. Methods Residency reputation data was acquired from the Doximity Residency Navigator website. Twitter and Instagram activity in 2019 and 2021 was analyzed by collecting data on the total number of posts and followers. Data on residency virtual recruitment was obtained from Twitter and UroResidency website. Results By the end of 2021, 122/139 (87.8%) urology residency programs had a Twitter accountand 61/139 (43.9%) had an Instagram account. A significant linear regression was found between Doximity ranking and the number of Twitter followers (p<0.001), Twitter posts (p=0.005), and Instagram followers (p=0.026). Virtual recruitment events were held by 107/139 (77%) programs in 2021. There was a significant linear regression between Doximity rankings and the number of virtual events (p<0.006). Conclusions Social media use by urology residency programs has increased since the COVID-19 pandemic. A program's higher Doximity ranking was correlated with the presence of Twitter and Instagram accounts as well as the number of Twitter followers, Twitter posts, and Instagram followers. There was a significant relationship between Doximity rankings and the number of hosted virtual events. Programs should consider increasing social media visibility to potentially improve their Doximity rankings.

The Impact of Social Media Use for Elected Parliamentarians: Evidence from Politicians' Use of Twitter During the Last Two Swiss Legislatures

AbstractNotwithstanding growing research on how using social media for political campaigning impacts politicians' chances of winning votes, we still have limited knowledge about whether and how the use of social media and online styles of communication affect political success over successive legislatures. We address this deficit by analyzing a panel dataset about the Twitter activity of politicians who have had a parliamentary mandate at least once. We first demonstrate that politicians' interaction with specific online audiences (e.g. in terms of replies and mentions) is still evolving, thus pointing to possible strategic adaptations of politicians' communication as social media are mastered. Then, we show that Twitter‐based activity moderately impacts politicians' political success, both in terms of political ranking and media coverage. This success, however, strongly depends on the style of political communication and on the legislature under scrutiny.

Pandemic Messaging: Congressional Communication and the Mechanisms of Polarizing Rhetoric

U.S. senators are increasingly turning to Twitter to stoke partisan divisions, and it’s not just what they say, but rather how they say it. Senators spent the Spring of 2020 responding to the COVID-19 pandemic, but the tone and framing used to engage a digital constituency was largely dependent on partisan alignment with President Trump. We use senators’ Twitter activity during the outbreak to offer new insight into the mechanisms of lawmakers’ party polarizing trends in congressional communication. We show that divisions stemmed from senators’ sentiment and framing—with Republicans more likely to incorporate positivity into forward-looking steps for economic recovery and Democrats preferring a negative tone to address government failings and inadequate response by President Trump. This article extends the literature on polarizing rhetoric in the Senate by using the pandemic response to illustrate how the dynamics of senators’ digital rhetoric, even during a moment of shared crisis, continue to fuel partisanship and polarizing narratives.

#Ophthalmology: Social Media Utilization and Impact in Ophthalmology Journals, Professional Societies, and Eye Health Organizations.

AimTo evaluate and quantify social media presence of ophthalmology peer-reviewed journals, professional societies, and eye health organizations, and to determine if there is a correlation between social media utilization and Twitter engagement metrics or journal impact measures.MethodsWe searched for online profiles of 100 ophthalmology peer-reviewed journals, 333 professional ophthalmology societies, and 40 eye health organizations on Facebook, Twitter, and Instagram. Impact was quantified by recording the number of “likes” on Facebook and number of followers on Twitter and Instagram. We also used Twitonomy software to obtain advanced Twitter metrics for all journal accounts from 2018 to 2021, and compared to journal impact measured by SCImago Journal Rank (SJR) score, the h-index, and impact factor.ResultsEye health organizations averaged significantly greater Facebooks “likes” and Twitter followers than both peer-reviewed journals and professional societies (p < 0.0001). Of 100 journals studied, 30% were active on Twitter, 25% on Facebook, and 6% on Instagram. Slightly more than half of all journal-affiliated social media accounts were accessible from the journal website. Among journals with active Twitter accounts, total followers, total tweets, average retweets, and average “favorites” were all significantly positively correlated with academic metrics such as the SJR, h-index, and/or impact factor.ConclusionGreater social media engagement is associated with higher ophthalmology journal impact metrics; however, ophthalmology journals and professional societies lag behind eye health organizations in social media engagement. Although unable to demonstrate causality, social media may be an underutilized visibility, communication, and dissemination tool.