Number Of Retweets Research Articles

Virality classification from Twitter data using pre-trained language model and multi-layer perceptron

Twitter is one of the well-known text-based social media that is often used to disseminate content. According to Katadata, Indonesia ranked fifth in the world in 2023. So many people or organizations want to make tweets go viral. Therefore, this research aims to develop a model that uses tweet data from the Indonesian language Twitter social media to categorize the level of virality. There are several tasks in classifying the level of virality, such as upsampling data, predicting sentiment and emotion, and text embedding. Upsampling data was carried out because the dataset used was an imbalanced dataset. Data upsampling, emotions, and text embedding is carried out using the bidirectional encoder representation from transformers (BERT) model. Meanwhile, sentiment prediction uses the Ro-bustly optimized BERT pretraining approach (RoBERTa). The results of text embedding, sentiment, emotion, will be combined with Twitter metadata then all features will be fed into the multi-layer perceptron (MLP) model to classifying the level of virality which is divided into 3 classes based on the number of retweets, namely low, medium and high. The proposed method produces an F1-score of 49% and an accuracy of 95% and performs better than the baseline model.

Social Media, Sentiments and Political Discourse ? An Exploratory Study of the 2021 Canadian Federal Election

Social media are widely used for online political discourse. Opinions shared on social media have different sentiments associated with them. Given the very high adoption rates of Twitter (now X) among adults, those who share their opinions on Twitter (X) not only represent a sizable segment of the society, but also influence (through emotion contagion) an even larger segment who are passive (non-contributing) users of the platform. Further, the discourse that is initiated on Twitter (X) typically spreads to other more traditional media. As a result, Twitter (X) is influential, which makes it useful to understand the factors related to the sentiments expressed in tweets. Such understanding can help policymakers to take actions that align with public needs and priorities. This research focuses on identifying the drivers (keywords) of sentiments associated with political discourse on Twitter (X). We also explore virality, i.e., how much a message (the tweet) spreads, and the relationship between sentiments and virality. Finally, we explore whether the clustering of tweets among sentiment and virality groups can improve the potential of social media content for predicting election results. Sentiment Analysis of 764,000 tweets related to the 2021 Canadian Federal election was followed by text clustering to identify sentiment-driving topics. We found some keywords predominantly present within a positive or negative sentiment that are suggestive of entities or ideas to invest in or mitigate by political decision makers. We were also able to find partial evidence for “negativity bias” by detecting a negative relationship between sentiment (positivity) and virality (number of retweets). Finally, we demonstrated that high positivity on the political discourse does not reflect election outcomes and examining Twitter (X) content in more neutral groups can improve predictive power. Our findings have implications for political decision makers and social media analytics researchers.

How negativity and policy content drive the spread of political messages

ABSTRACT Research demonstrates that negative messages spread more online, both at the elite and mass levels. We know comparatively less about the role that policy content plays, and whether that might be responsible for the effect of negativity. We examine over 1.4 million congressional tweets to test the effect of message tone and topic on the number of retweets that messages receive. We find that elite messages are retweeted more when they contain political attacks. However, even while controlling for tone, we find that messages are shared more when they discuss some “culture war” issues or when they discuss Donald Trump.

Sentiment analysis of tweet content on Hurricane Dorian: Sensemaking in digital journalistic inquiry ecology

Twitter is a powerful digital journalistic instrument and evidence shows journalists were transferring authority to Twitter. With journalistic information ecology becoming imbalanced, it is valuable to research how journalists may use Twitter to discover accurate and reliable information and maintain a vast overview of news events without shifting the power as the fourth estate. The purpose of this study is to provide a possible digital journalistic inquiry model to identify trending topics, distinguish reliable journalistic information while maintaining the balance of journalistic information ecology. Utilizing a large-scale dataset – 1.2 million tweets collected from Twitter API – this study executed cutting-edge network analysis and sentiment analysis to fill in the knowledge gap through a case study on Hurricane Dorian. The study found that the impact of traditional opinion leaders on information diffusion is declining. On the contrary, top in-degree centrality users play more important roles in information diffusion on Twitter. Moreover, tweets with negative polarity opinions were retweeted more. In addition, non-opinion leaders’ negatively polarized tweets were retweeted more than positively polarized ones, although it is not the same case with opinion leaders. With the change of journalistic ecology, identifying top in-degree centrality users and examining their tweets will provide useful resources for journalists to identify keywords, trending themes and predict how likely a topic may interest audience based on degree of polarity and number of retweets on Twitter. The results provide useful patterns for journalists to follow in sensemaking tasks in digital journalistic inquiry.

Diffusion of tax-related communication on social media

Taxation is a recurrent topic in people's conversations, also on social media. Yet, informal channels such as social media have been widely neglected in studies that examined how information about taxation spreads across social networks. Using posts on Twitter (currently called “X”) with taxation related hashtags from 2010 to 2020, we examined what linguistic cues are associated with information diffusion, that is, the number of retweets a message receives. The use of emotional, moral, and moral-emotional language in a tweet was associated with greater diffusion (i.e., more retweets). In contrast to the negativity bias literature, positive emotional words were more strongly associated with information diffusion than negative emotional words. Among the specific emotions that taxation research has focused on, only the use of anger (but not anxiety) words was associated with more retweets. The study contributes to the literature by examining individuals’ reasoning about taxes.

Social media-based demographic and sentiment analysis for disaster responses.

This study explores disaster responses across the United States for Winter Storm Jaxon in 2018 by utilizing demographic and sentiment analysis for Twitter®. This study finds that people show highly fluctuated responses across the study periods and highest natural sentiment, followed by positive sentiment and negative sentiment. Also, some sociodemographic and Twitter variables, such as gender and long text, are strongly related to human sentiment, whereas other sociodemographic and Twitter variables, such as age and the higher number of retweets, are not associated with it. The results show that governments and disaster experts should consider a multitude of sociodemographic and Twitter variables to understand human responses and sentiment during natural disaster events.

The adaptive community-response (ACR) method for collecting misinformation on social media

Social media can be a major accelerator of the spread of misinformation, thereby potentially compromising both individual well-being and social cohesion. Despite significant recent advances, the study of online misinformation is a relatively young field facing several (methodological) challenges. In this regard, the detection of online misinformation has proven difficult, as online large-scale data streams require (semi-)automated, highly specific and therefore sophisticated methods to separate posts containing misinformation from irrelevant posts. In the present paper, we introduce the adaptive community-response (ACR) method, an unsupervised technique for the large-scale collection of misinformation on Twitter (now known as ’X’). The ACR method is based on previous findings showing that Twitter users occasionally reply to misinformation with fact-checking by referring to specific fact-checking sites (crowdsourced fact-checking). In a first step, we captured such misinforming but fact-checked tweets. These tweets were used in a second step to extract specific linguistic features (keywords), enabling us to collect also those misinforming tweets that were not fact-checked at all as a third step. We initially present a mathematical framework of our method, followed by an explicit algorithmic implementation. We then evaluate ACR on the basis of a comprehensive dataset consisting of >25\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$>25$$\\end{document} million tweets, belonging to >300\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$>300$$\\end{document} misinforming stories. Our evaluation shows that ACR is a useful extension to the methods pool of the field, enabling researchers to collect online misinformation more comprehensively. Text similarity measures clearly indicated correspondence between the claims of false stories and the ACR tweets, even though ACR performance was heterogeneously distributed across the stories. A baseline comparison to the fact-checked tweets showed that the ACR method can detect story-related tweets to a comparable degree, while being sensitive to different types of tweets: Fact-checked tweets tend to be driven by high outreach (as indicated by a high number of retweets), whereas the sensitivity of the ACR method extends to tweets exhibiting lower outreach. Taken together, ACR’s capacity as a valuable methodological contribution to the field is based on (i) the adoption of prior, pioneering research in the field, (ii) a well-formalized mathematical framework and (iii) an empirical foundation via a comprehensive set of indicators.

Political context of the European vaccine debate on Twitter

At the beginning of the COVID-19 pandemic, fears grew that making vaccination a political (instead of public health) issue may impact the efficacy of this life-saving intervention, spurring the spread of vaccine-hesitant content. In this study, we examine whether there is a relationship between the political interest of social media users and their exposure to vaccine-hesitant content on Twitter. We focus on 17 European countries using a multilingual, longitudinal dataset of tweets spanning the period before COVID, up to the vaccine roll-out. We find that, in most countries, users’ endorsement of vaccine-hesitant content is the highest in the early months of the pandemic, around the time of greatest scientific uncertainty. Further, users who follow politicians from right-wing parties, and those associated with authoritarian or anti-EU stances are more likely to endorse vaccine-hesitant content, whereas those following left-wing politicians, more pro-EU or liberal parties, are less likely. Somewhat surprisingly, politicians did not play an outsized role in the vaccine debates of their countries, receiving a similar number of retweets as other similarly popular users. This systematic, multi-country, longitudinal investigation of the connection of politics with vaccine hesitancy has important implications for public health policy and communication.

A multidimensional comparative study of help-seeking messages on Weibo under different stages of COVID-19 pandemic in China.

During the COVID-19 pandemic, people posted help-seeking messages on Weibo, a mainstream social media in China, to solve practical problems. As viruses, policies, and perceptions have all changed, help-seeking behavior on Weibo has been shown to evolve in this paper. We compare and analyze the help-seeking messages from three dimensions: content categories, time distribution, and retweeting influencing factors. First, we crawled the help-seeking messages from Weibo, and successively used CNN and xlm-roberta-large models for text classification to analyze the changes of help-seeking messages in different stages from the content categories dimension. Subsequently, we studied the time distribution of help-seeking messages and calculated the time lag using TLCC algorithm. Finally, we analyze the changes of the retweeting influencing factors of help-seeking messages in different stages by negative binomial regression. (1) Help-seekers in different periods have different emphasis on content. (2) There is a significant correlation between new daily help-seeking messages and new confirmed cases in the middle stage (1/1/2022-5/20/2022), with a 16-day time lag, but there is no correlation in the latter stage (12/10/2022-2/25/2023). (3) In all the periods, pictures or videos, and the length of the text have a significant positive effect on the number of retweets of help-seeking messages, but other factors do not have exactly the same effect on the retweeting volume. This paper demonstrates the evolution of help-seeking messages during different stages of the COVID-19 pandemic in three dimensions: content categories, time distribution, and retweeting influencing factors, which are worthy of reference for decision-makers and help-seekers, as well as provide thinking for subsequent studies.

Audience reconstructed: social media interaction by BTS fans during live stream concerts.

COVID-19-motivated social distancing made online concerts common practice in 2020 and 2021, with millions logging into streaming sites to see their favorite artists perform in realtime. For some fans, watching alone at home may have been enough, but concert-concurrent surges of social media activity suggest many virtual performance attendees are doing more. To understand why fans would turn their attention from these precious performance streams to social media, we explored Twitter engagement during four live streamed concerts performed by the Kpop group BTS in 2021. In public Tweets sampled by either concert hashtag or a predefined stream of users and keywords, we evaluated patterns in posting rates in relation to concert program events and investigated the content patterns in 1,200 Tweets sampled from four ranges of popularity (number of Retweets during the concert). Across concerts, short "Shout" Tweets surged at the start of songs, while the rate of retweets often fell during musical performances and shot up when BTS was off stage. Content analysis on the subsample found the materials most widely shared were informational or featured concert visuals, mimicking how fans use their phones at in-person concerts. Most original posts received few Retweets and were more personal and expressive of admiration for the performers. Comparison between the samples (concert hashtag vs. stream) also suggests users were strategic in using or omitting official concert hashtags with the strongest differences in the most widely disseminated content. Postings on Twitter during these performances seemed principally directed to fellow fans and audience members, by individuals choosing to share their own excitement and check in with others. By leveraging their existing social media networks, these concert attendees constructed a collective and interactive concert space, connecting with friends and strangers in the crowd and helping each other capture a richer experience than any broadcasting platform currently supports.

Emotions and Information Diffusion on Social Media: A Replication in the Context of Political Communication on Twitter

This paper presents a methodological and conceptual replication of Stieglitz and Dang-Xuan’s (2013) investigation of the role of sentiment in information-sharing behavior on social media. Whereas Stieglitz and Dang-Xuan (2013) focused on Twitter communication prior to the state parliament elections in the German states Baden-Wurttemberg, Rheinland-Pfalz, and Berlin in 2011, we test their theoretical propositions in the context of the state parliament elections in Saxony-Anhalt (Germany) 2021. We confirm the positive link between sentiment in a political Twitter message and its number of retweets in a methodological replication. In a conceptual replication, where sentiment was assessed with the alternative dictionary-based tool LIWC, the sentiment was negatively associated with the retweet volume. In line with the original study, the strength of association between sentiment and retweet time lag insignificantly differs between tweets with negative sentiment and tweets with positive sentiment. We also found that the number of an author’s followers was an essential determinant of sharing behavior. However, two hypotheses supported in the original study did not hold for our sample. Precisely, the total amount of sentiments was insignificantly linked to the time lag to the first retweet. Finally, in our data, we do not observe that the association between the overall sentiment and retweet quantity is stronger for tweets with negative sentiment than for those with positive sentiment.

Science communication on X (formerly Twitter): A picture is worth a thousand characters?

X (formerly Twitter), a microblogging social media platform, is being used by scientists and researchers to disseminate their research findings and promote the visibility of their work to the public. Tweets can be posted with text messages, images, hyperlinks, or a combination of these features. Importantly, for the majority of users, the text must be limited to 280 characters. In this perspective, this study aimed to observe if adding an image is able to increase outreach for scientific communication on X. Therefore, the characteristics of tweets posted with the hashtag #SciComm (short for science communication) for a period of one year (28 May 2020 to 28 May 2021) were analyzed with the X analytics tool Symplur Signals. The conducted analysis revealed that when a science communication (#SciComm-containing) tweet is accompanied by an image added by the user, there is on average a 529% increase in the number of retweets, and adding a hyperlink is similarly effective in increasing the number of retweets. However, combining both an image and hyperlink in the same tweet did not yield an additive effect. Hence, for increased visibility, researchers may consider adding images or hyperlinks (e.g., to research publications or popular science articles) while communicating science to the public on X.

Public perception of the resumption of HPV vaccine recommendation in Japan: Twitter content analysis.

On 12 November 2021, after a stagnation of 8.5 years, Japan decided to resume proactive recommendations for HPV vaccines. However, it is not known how the public reacted to the resumption of proactive recommendations for HPV vaccines, which are key elements in health communication that promote HPV vaccination. This study aimed to capture discussions on HPV vaccination on Twitter and to identify user characteristics, sentiments, discussed themes and their potential reach before and after 12 November 2021, when the Ministry of Health, Labour, and Welfare's decision to resume proactive recommendation for HPV vaccines was first reported by the media in Japan. This study conducted a content analysis of tweets related to HPV vaccines posted on 11 November and 12 November 2021. Tweets were coded in terms of user characteristics, sentiments (i.e. positive, negative and neutral) and themes. Reach was measured by the number of retweets, likes and followers. A total of 3623 tweets were identified. The results showed that approximately 50% of health professionals and researchers tweeted neutral content about HPV vaccines. The most frequently discussed theme was the safety and side effects of HPV vaccines, which was accompanied by mainly negative sentiments. Although health professionals and researchers are influential on Twitter, half of them tweeted neutrally about HPV vaccines. Influential professionals, such as health professionals and researchers, are expected to disseminate accurate information to correct misinformation and recommend HPV vaccination on Twitter to overcome the HPV vaccination crisis, which is characterized by the low vaccination rates.

Fact-Checking Cancer Information on Social Media in Japan: Retrospective Study Using Twitter.

The widespread use of social media has made it easier for patients to access cancer information. However, a large amount of misinformation and harmful information that could negatively impact patients' decision-making is also disseminated on social media platforms. We aimed to determine the actual amount of misinformation and harmful information as well as trends in the dissemination of cancer-related information on Twitter, a representative social media platform. Our findings can support decision-making among Japanese patients with cancer. Using the Twitter app programming interface, we extracted tweets containing the term "cancer" in Japanese that were posted between August and September of 2022. The eligibility criteria were the cancer-related tweets with the following information: (1) reference to the occurrence or prognosis of cancer, (2) recommendation or nonrecommendation of actions, (3) reference to the course of cancer treatment or adverse events, (4) results of cancer research, and (5) other cancer-related knowledge and information. Finally, we selected the top 100 tweets with the highest number of "likes." For each tweet, 2 independent reviewers evaluated whether the information was factual or misinformation, and whether it was harmful or safe with the reasons for the decisions on the misinformation and harmful tweets. Additionally, we examined the frequency of information dissemination using the number of retweets for the top 100 tweets and investigated trends in the dissemination of information. The extracted tweets totaled 69,875. Of the top 100 cancer-related tweets with the most "likes" that met the eligibility criteria, 44 (44%) contained misinformation, 31 (31%) contained harmful information, and 30 (30%) contained both misinformation and harmful information. Misinformation was described as Unproven (29/94, 40.4%), Disproven (19/94, 20.2%), Inappropriate application (4/94, 4.3%), Strength of evidence mischaracterized (14/94, 14.9%), Misleading (18/94, 18%), and Other misinformation (1/94, 1.1%). Harmful action was described as Harmful action (9/59, 15.2%), Harmful inaction (43/59, 72.9%), Harmful interactions (3/59, 5.1%), Economic harm (3/59, 5.1%), and Other harmful information (1/59, 1.7%). Harmful information was liked more often than safe information (median 95, IQR 43-1919 vs 75.0 IQR 43-10,747; P=.04). The median number of retweets for the leading 100 tweets was 13.5 (IQR 0-2197). Misinformation was retweeted significantly more often than factual information (median 29.0, IQR 0-502 vs 7.5, IQR 0-2197; P=.01); harmful information was also retweeted significantly more often than safe information (median 35.0, IQR 0-502 vs 8.0, IQR 0-2197; P=.002). It is evident that there is a prevalence of misinformation and harmful information related to cancer on Twitter in Japan and it is crucial to increase health literacy and awareness regarding this issue. Furthermore, we believe that it is important for government agencies and health care professionals to continue providing accurate medical information to support patients and their families in making informed decisions.

Does Twitter improve the dissemination of ophthalmology scientific publications?

PurposeTo determine whether Twitter improves dissemination of ophthalmology scientific publicationsMethodsData were collected on articles published on PubMed between the years 2016 and 2021 (inclusive) and identified with the word “ophthalmology”. Twitter performance metrics, including the number of tweets, number of likes, and number of retweets were collected from Twitter using the publicly available scientific API. Machine learning and descriptive statistics were used to outline Twitter performance metrics.ResultsThe number of included articles was 433710. The percentage of articles that were in the top quartile for citation count, which had ≥1 tweet was 34.4% (number 437/1270). Conversely, the percentage of articles that were in the top quartile for citation count, which had 0 tweets was 27.8% (number 12023/43244). When machine learning was used to predict Twitter performance metrics an AUROC of 0.78 was returned. This was associated with an accuracy of 0.97ConclusionThis study has shown preliminary evidence to support that Twitter may improve the dissemination of scientific ophthalmology publications.

Mobilizing Media Attention in the Era of Networked Publics: A Contentious Publicness Framework

ABSTRACT Guided by the framework of contentious publicness, this study examines the role of mass media organizations in facilitating digital activism during the 2019–2020 Hong Kong anti-extradition movement. We collected movement tweets targeting media organizations, conceptualized as public-media messages, to identify what media outlets and what narratives were likely to generate more engagement. Findings revealed that activists tended to mobilize media based on colonial history and legitimacy. Furthermore, topic areas, media type, and technological affordances were all related to message publicness measured by the number of retweets and likes. Implications are provided regarding the process of mobilizing for contentious publicness.

Retweet Prediction Based on User-Based, Content-Based, Time-Based Features Using ANN Classification Optimized with the Bat Algorithm

Twitter is one of the most popular social media platforms today for information dissemination. It is favored by the public due to its real-time information sharing capabilities. Twitter provides two important features for information dissemination: Tweets and Retweets. Tweets allow users to write messages that can be instantly shared. Each tweet can contain text, media such as images, videos, or URLs. Retweets allow users to repost someone else's tweet and distribute it to their own followers. The Retweet feature is considered an effective way to spread information, as a high number of retweets indicates that the information in the tweet is spreading quickly and widely. This research aims to predict retweets based on several features: User-Based Feature, Content-Based Feature, and Time-Based Feature. The classification method used is Artificial Neural Network, which is optimized using a Nature-Inspired Algorithm called Bat Algorithm. The evaluation results of this study show an accuracy of 86%, precision of 87.8%, recall of 93.6%, and F1-score of 90.6% without imbalance class handling. Under Undersampling condition, the accuracy is 80.8%, precision is 91.0%, recall is 81.4%, and F1-score is 85.9%. Under Oversampling condition, the accuracy is 82.4%, precision is 89.6%, recall is 85.6%, and F1-score is 87.5%. These results indicate that using user-based, content-based, and time-based features, applying Artificial Neural Network classification method, and optimizing hyperparameters using Bat Algorithm are effective in predicting retweets.

Impact of Tweet Content on the Number of Retweets - "Tweet the Meeting 2022".

Background: Previous research has investigated the effectiveness of the "Tweet the Meeting" campaign, but the relationship between tweet content and the number of retweets has not been fully evaluated. Methods and Results: We analyzed the number of tweets and retweets during the Japanese Circulation Society's 2022 annual meeting. The ambassador group had significantly more session- and symposium-related tweets than the non-ambassador group (P<0.001), associated with the nubmer of retweets. Symposium-related tweets with figures generated more retweets than those without figures (mean [±SD] 3.47±3.31 vs. 2.48±1.94 retweets per tweet, respectively; P=0.001). Conclusions: The study revealed that official meeting-designated Twitter ambassadors disseminate more educational content than non-ambassadors, and generated more retweets.

The effects of algorithmic content selection on user engagement with news on Twitter

In this article, we investigate how Twitter’s switch from a reverse-chronological timeline to algorithmic content selection in March 2016 influenced user engagement with tweets published by German newspapers. To mitigate concerns about omitted variables, we use the Facebook postings of these newspapers as a counterfactual. We find that the number of likes increased by 20% and the number of retweets by 15% within a span of 30 days after the switch. Importantly, our results indicate a rich-get-richer effect, implying that initially more popular outlets and news topics benefited the most. User engagement also increased more for sensationalist content than quality news stories.

Twitter and the protest movement in Thailand: A thematic analysis of highly retweeted tweets during the pro-democracy protests

In 2021, there were over 1,516 street protests across Thailand, with government forces deployed to disperse protests over 60 times nationwide. Twitter, like the physical protest sites, became an extremely active platform. This paper aims to understand key content with the highest number of retweets during the call for democracy in Thailand in 2021, specifically under the hashtags #28FebMob, #18JulMob, and #7AugMob, through thematic analysis. The findings indicate five key themes conveyed through tweets, namely information dissemination, grievance expression, moral support, mobilization, and calls for action. The study also shows that most of the content conveyed frustration against state power and was highly subjective rather than stating protest objectives or calling for more forces. The characteristics of the tweets were also related to ongoing circumstances of the actual protests at the time, which differed widely depending on the specific event.