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This study presents a secondary data analysis of the survey data collected as part of the American Trends Panel series by the Pew Research Center. A logistic regression was performed to ascertain the effects of the perceived risk of sharing, perceived problems on Twitter, and motivation of using Twitter on the likelihood that participants regret sharing on Twitter. The logistic regression model was statistically significant, \c{hi}2(15) = 102.5, p < .001. The model correctly classified 78.5 percent of cases. Whether or not Twitter users regret sharing on Twitter depends on different motivations for using Twitter. We observe that "A way to express my opinion" is statistically significant in the mod-el, indicating that the odds of Twitter users regretting sharing for this motivation is 2.1 times higher than that of entertainment. Perceived risks of potential hostility and visibility were negatively associated with an increased likelihood of regret sharing. In contrast, perceived problems on Twitter concerning misinformation were negatively associated with the likelihood of regret sharing.

With the advent of online social media, phishers have started using social networks like Twitter, Facebook, and Foursquare to spread phishing scams. Twitter is an immensely popular micro-blogging network where people post short messages of 140 characters called tweets. It has over 100 million active users who post about 200 million tweets everyday. Phishers have started using Twitter as a medium to spread phishing because of this vast information dissemination. Further, it is difficult to detect phishing on Twitter unlike emails because of the quick spread of phishing links in the network, short size of the content, and use of URL obfuscation to shorten the URL. Our technique, PhishAri, detects phishing on Twitter in realtime. We use Twitter specific features along with URL features to detect whether a tweet posted with a URL is phishing or not. Some of the Twitter specific features we use are tweet content and its characteristics like length, hashtags, and mentions. Other Twitter features used are the characteristics of the Twitter user posting the tweet such as age of the account, number of tweets, and the follower-followee ratio. These Twitter specific features coupled with URL

This paper compares a PyCaret AutoML branch and a CNN-BiLSTM branch for binary hate speech detection on Indonesian Twitter using the HS label from the corpus of Ibrohim and Budi. Both branches share the same preprocessing pipeline so that the comparison reflects modelling differences rather than inconsistent data preparation. The conventional branch uses TF-IDF with a lexicon-based abusive-word count, whereas the neural branch learns dense token representations and captures both local phrase patterns and bidirectional context. The benchmark is built from the released 13,130-row annotation table, whose HS label yields a 58:42 class ratio. On the held-out split, CNN-BiLSTM achieves the best result with 83.8% accuracy, 79.8% precision, 82.7% recall, and 81.2% F1-score. Within the PyCaret branch, Random Forest is the strongest conventional model with 77.2% accuracy and 77.0% F1-score. The neural branch therefore improves accuracy by 6.6 points and F1-score by 4.2 points. Exploratory corpus analysis, learning curves, and confusion matrices show that the dataset is short-text, moderately imbalanced, and still difficult because many decisions depend on local lexical cues plus short contex

The acquisition of Twitter by Elon Musk has spurred controversy and uncertainty among Twitter users. The move raised as many praises as concerns, particularly regarding Musk's views on free speech. As a result, a large number of Twitter users have looked for alternatives to Twitter. Mastodon, a decentralized micro-blogging social network, has attracted the attention of many users and the general media. In this paper, we track and analyze the migration of 136,009 users from Twitter to Mastodon. Our analysis sheds light on the user-driven pressure towards centralization in a decentralized ecosystem and identifies the strong influence of the social network in platform migration. We also characterize the activity of migrated users on both Twitter and Mastodon.

In this paper we present a novel methodology for identifying scholars with a Twitter account. By combining bibliometric data from Web of Science and Twitter users identified by Altmetric.com we have obtained the largest set of individual scholars matched with Twitter users made so far. Our methodology consists of a combination of matching algorithms, considering different linguistic elements of both author names and Twitter names; followed by a rule-based scoring system that weights the common occurrence of several elements related with the names, individual elements and activities of both Twitter users and scholars matched. Our results indicate that about 2% of the overall population of scholars in the Web of Science is active on Twitter. By domain we find a strong presence of researchers from the Social Sciences and the Humanities. Natural Sciences is the domain with the lowest level of scholars on Twitter. Researchers on Twitter also tend to be younger than those that are not on Twitter. As this is a bibliometric-based approach, it is important to highlight the reliance of the method on the number of publications produced and tweeted by the scholars, thus the share of scholars o

In the marketing of video games made by self-publishing, independent developers, there is common advice to market a game using the social media platform Twitter. However, the advice that stems from industry sources is somewhat contradictory, and many self-publishing independent developers elect not to use Twitter at all. This presents an opportunity for researchers to investigate this tension and determine if using Twitter does have a causal influence on the successful release of a game by using relatively recent developments in causal data science techniques. In this sense, this paper highlights these causal inference developments while simultaneously informing self-publishing independent developers whether they should indeed market their games using Twitter. It was found that using Twitter results in an average increase of 85.4 reviews during release week, corresponding to a 314% positive difference. Using Twitter also doubles the chance of reaching a critical 10-review inflection point threshold on release week. These effects, however, are moderated by the characteristics of the game, expressed as 'tags', yet in no case is the effect of Twitter use reduced to 0. Based on these f

We combine user-centric Twitter data with video-centric YouTube data to analyze who watches and shares what on YouTube. Combination of two data sets, with 87k Twitter users, 5.6mln YouTube videos and 15mln video sharing events, allows rich analysis going beyond what could be obtained with either of the two data sets individually. For Twitter, we generate user features relating to activity, interests and demographics. For YouTube, we obtain video features for topic, popularity and polarization. These two feature sets are combined through sharing events for YouTube URLs on Twitter. This combination is done both in a user-, a video- and a sharing-event-centric manner. For the user-centric analysis, we show how Twitter user features correlate both with YouTube features and with sharing-related features. As two examples, we show urban users are quicker to share than rural users and for some notions of "influence" influential users on Twitter share videos with a higher number of views. For the video-centric analysis, we find a superlinear relation between initial Twitter shares and the final amounts of views, showing the correlated behavior of Twitter. On user impact, we find the total a

We examine the influence of Twitter promotion on cryptocurrency pump-and-dump events. By analyzing abnormal returns, trading volume, and tweet activity, we uncover that Twitter effectively garners attention for pump-and-dump schemes, leading to notable effects on abnormal returns before the event. Our results indicate that investors relying on Twitter information exhibit delayed selling behavior during the post-dump phase, resulting in significant losses compared to other participants. These findings shed light on the pivotal role of Twitter promotion in cryptocurrency manipulation, offering valuable insights into participant behavior and market dynamics.

Twitter provides an open and rich source of data for studying human behaviour at scale and is widely used in social and network sciences. However, a major criticism of Twitter data is that demographic information is largely absent. Enhancing Twitter data with user ages would advance our ability to study social network structures, information flows and the spread of contagions. Approaches toward age detection of Twitter users typically focus on specific properties of tweets, e.g., linguistic features, which are language dependent. In this paper, we devise a language-independent methodology for determining the age of Twitter users from data that is native to the Twitter ecosystem. The key idea is to use a Bayesian framework to generalise ground-truth age information from a few Twitter users to the entire network based on what/whom they follow. Our approach scales to inferring the age of 700 million Twitter accounts with high accuracy.

This study verified the effectiveness of Donald Trump's Twitter campaign in guiding agen-da-setting and deflecting political risk and examined Trump's Twitter communication strategy and explores the communication effects of his tweet content during Covid-19 pandemic. We collected all tweets posted by Trump on the Twitter platform from January 1, 2020 to December 31, 2020.We used Ordinary Least Squares (OLS) regression analysis with a fixed effects model to analyze the existence of the Twitter strategy. The correlation between the number of con-firmed daily Covid-19 diagnoses and the number of particular thematic tweets was investigated using time series analysis. Empirical analysis revealed Twitter's strategy is used to divert public attention from negative Covid-19 reports during the epidemic, and it posts a powerful political communication effect on Twitter. However, findings suggest that Trump did not use false claims to divert political risk and shape public opinion.

Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-know micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known "event study" from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the "event study" methodology to relate them to stock returns. We show that sent

With the rise of social media platforms, especially X.com (formerly Twitter), there is a growing interest in understanding digital social networks and human digital interactions. This paper presents a comprehensive methodology for extracting, processing, and visually analyzing data from X.com, using a combination of Python and R packages, enhanced by our publicly accessible, customizable code. Our approach compiles a dynamic dataset that captures various interactions: replies, retweets, and mentions. To explore deeper insights, the data is subjected to sentiment analysis and keyword coding, indicating shifts in discourse over time. Our method is structured in three primary phases. Initially, R is employed for pulling data and the formation of social network datasets. Following this, the combination of Python and R is utilized for sentiment analysis and keyword coding, aiming to uncover the underlying emotional shifts and language transitions within topics of discussion. The final phase employs R to visualize the dynamic shifts within these social networks. These visualization tools highlight changes in user interactions and patterns of influence. For a practical demonstration, we a

This paper investigates the stability of Twitter counts of scientific publications over time. For this, we conducted an analysis of the availability statuses of over 2.6 million Twitter mentions received by the 1,154 most tweeted scientific publications recorded by Altmetric.com up to October 2017. Results show that of the Twitter mentions for these highly tweeted publications, about 14.3% have become unavailable by April 2019. Deletion of tweets by users is the main reason for unavailability, followed by suspension and protection of Twitter user accounts. This study proposes two measures for describing the Twitter dissemination structures of publications: Degree of Originality (i.e., the proportion of original tweets received by a paper) and Degree of Concentration (i.e., the degree to which retweets concentrate on a single original tweet). Twitter metrics of publications with relatively low Degree of Originality and relatively high Degree of Concentration are observed to be at greater risk of becoming unstable due to the potential disappearance of their Twitter mentions. In light of these results, we emphasize the importance of paying attention to the potential risk of unstable T

This paper investigates the interplay between different types of user interactions on Twitter, with respect to predicting missing or unseen interactions. For example, given a set of retweet interactions between Twitter users, how accurately can we predict reply interactions? Is it more difficult to predict retweet or quote interactions between a pair of accounts? Also, how important is time locality, and which features of interaction patterns are most important to enable accurate prediction of specific Twitter interactions? Our empirical study of Twitter interactions contributes initial answers to these questions. We have crawled an extensive dataset of Greek-speaking Twitter accounts and their follow, quote, retweet, reply interactions over a period of a month. We find we can accurately predict many interactions of Twitter users. Interestingly, the most predictive features vary with the user profiles, and are not the same across all users. For example, for a pair of users that interact with a large number of other Twitter users, we find that certain "higher-dimensional" triads, i.e., triads that involve multiple types of interactions, are very informative, whereas for less active

Twitter has arguably been the most popular among the data sources that form the basis of so-called altmetrics. Tweets to scholarly documents have been heralded as both early indicators of citations as well as measures of societal impact. This chapter provides an overview of Twitter activity as the basis for scholarly metrics from a critical point of view and equally describes the potential and limitations of scholarly Twitter metrics. By reviewing the literature on Twitter in scholarly communication and analyzing 24 million tweets linking to scholarly documents, it aims to provide a basic understanding of what tweets can and cannot measure in the context of research evaluation. Going beyond the limited explanatory power of low correlations between tweets and citations, this chapter considers what types of scholarly documents are popular on Twitter, and how, when and by whom they are diffused in order to understand what tweets to scholarly documents measure. Although this chapter is not able to solve the problems associated with the creation of meaningful metrics from social media, it highlights particular issues and aims to provide the basis for advanced scholarly Twitter metrics.

Big data research is currently split on whether and to what extent Twitter can be characterised as an informational or social network. We contribute to this line of inquiry through an investigation of digital humanities scholars' uses and gratifications of Twitter. We conducted a thematic analysis of 25 semistructured interview transcripts to learn about these scholars' professional use of Twitter. Our findings show that Twitter is considered a critical tool for informal communication within DH invisible colleges, functioning at varying levels as both an informational network (learning to 'Twitter' and maintaining awareness) and a social network (imagining audiences and engaging other digital humanists). We find that Twitter follow relationships reflect common academic interests and are closely tied to scholars' preexisting social ties and conference or event co-attendance. The concept of the invisible college continues to be relevant but requires revisiting. The invisible college formed on Twitter is messy, consisting of overlapping social contexts (professional, personal, and public), scholars with different habits of engagement, and both formal and informal ties. Our research il

Gender analysis of Twitter can reveal important socio-cultural differences between male and female users. There has been a significant effort to analyze and automatically infer gender in the past for most widely spoken languages' content, however, to our knowledge very limited work has been done for Arabic. In this paper, we perform an extensive analysis of differences between male and female users on the Arabic Twitter-sphere. We study differences in user engagement, topics of interest, and the gender gap in professions. Along with gender analysis, we also propose a method to infer gender by utilizing usernames, profile pictures, tweets, and networks of friends. In order to do so, we manually annotated gender and locations for ~166K Twitter accounts associated with ~92K user location, which we plan to make publicly available at http://anonymous.com. Our proposed gender inference method achieve an F1 score of 82.1%, which is 47.3% higher than majority baseline. In addition, we also developed a demo and made it publicly available.

Twitter with over 500 million users globally, generates over 100,000 tweets per minute . The 140 character limit per tweet, perhaps unintentionally, encourages users to use shorthand notations and to strip spellings to their bare minimum "syllables" or elisions e.g. "srsly". The analysis of twitter messages which typically contain misspellings, elisions, and grammatical errors, poses a challenge to established Natural Language Processing (NLP) tools which are generally designed with the assumption that the data conforms to the basic grammatical structure commonly used in English language. In order to make sense of Twitter messages it is necessary to first transform them into a canonical form, consistent with the dictionary or grammar. This process, performed at the level of individual tokens ("words"), is called lexical normalisation. This paper investigates various techniques for lexical normalisation of Twitter data and presents the findings as the techniques are applied to process raw data from Twitter.

On daily basis, millions of Twitter accounts post a vast number of tweets including numerous Twitter entities (mentions, replies, hashtags, photos, URLs). Many of these entities are used in common by many accounts. The more common entities are found in the messages of two different accounts, the more similar, in terms of content or interest, they tend to be. Towards this direction, we introduce a methodology for discovering and suggesting similar Twitter accounts, based entirely on their disseminated content in terms of Twitter entities used. The methodology is based exclusively on semantic representation protocols and related technologies. An ontological schema is also described towards the semantification of the Twitter accounts and their entities.