Functions of personal experience and of expression of regret

Although learning and preparing for future behavior are well-established functions of regret, social functions have been largely ignored. We suggest a new model of the functions of regret, the Privately Experienced versus Expressed Regret (PEER) model, in which private experience and public expression differentially serve these functions. The current research examined this model using both naturalistic and experimental approaches. In Study 1, we coded tweets about regret posted on social media to examine whether this content emphasized social relationships versus learning and preparation. Study 2 experimentally examined the hypothesized social-closeness function for expression of regrets. Study 3 further examined how privately experienced and publicly expressed regrets differ on the social-closeness and learning and preparatory functions. Studies 4 and 5 confirmed the specific social closeness function rather than global social benefits. This research suggests that the social expression of regret differs from private experience in both form and function

Using Twitter Post Data to Ascertain the Sentiment of Alcohol-related Blackouts in the United States

Research shows variability in how alcohol-related blackouts (periods of memory loss during/after drinking) are subjectively evaluated. We accessed 3.5 million original Tweets written in the U.S. between July 2009 and February 2020 that referenced blackouts, and coded the sentiment (positive or negative) of those Tweets, using the machine learning function of a Twitter-sponsored commercial platform. The sentiment of Tweets was examined by day of week and compared to the sentiment of blackout Tweets on certain holidays to non-celebration matched days. Tweets were more likely to have a positive (73%) than negative sentiment, and positive Tweets were more common during weekends. Relative to typical non-celebratory weekends, a greater proportion of blackout Tweets were positive around Thanksgiving and New Year’s Eve, though differences were not observed relative to several other celebratory periods (e.g., Superbowl). Results have implications for online interventions, which can use social networking sites to target alcohol during high-risk periods

Manipulating Twitter Through Deletions

Research into influence campaigns on Twitter has mostly relied on identifying malicious activities from tweets obtained via public APIs. These APIs provide access to public tweets that have not been deleted. However, bad actors can delete content strategically to manipulate the system. Unfortunately, estimates based on publicly available Twitter data underestimate the true deletion volume. Here, we provide the first exhaustive, large-scale analysis of anomalous deletion patterns involving more than a billion deletions by over 11 million accounts. We find that a small fraction of accounts delete a large number of tweets daily. We also uncover two abusive behaviors that exploit deletions. First, limits on tweet volume are circumvented, allowing certain accounts to flood the network with over 26 thousand daily tweets. Second, coordinated networks of accounts engage in repetitive likes and unlikes of content that is eventually deleted, which can manipulate ranking algorithms. These kinds of abuse can be exploited to amplify content and inflate popularity, while evading detection. Our study provides platforms and researchers with new methods for identifying social media abuse

Detecting and identifying the reasons for deleted tweets before they are posted

Social media platforms empower us in several ways, from information dissemination to consumption. While these platforms are useful in promoting citizen journalism, public awareness, etc., they have misuse potential. Malicious users use them to disseminate hate speech, offensive content, rumor, etc. to promote social and political agendas or to harm individuals, entities, and organizations. Oftentimes, general users unconsciously share information without verifying it or unintentionally post harmful messages. Some of such content often gets deleted either by the platform due to the violation of terms and policies or by users themselves for different reasons, e.g., regret. There is a wide range of studies in characterizing, understanding, and predicting deleted content. However, studies that aim to identify the fine-grained reasons (e.g., posts are offensive, hate speech, or no identifiable reason) behind deleted content are limited. In this study, we address an existing gap by identifying and categorizing deleted tweets, especially within the Arabic context. We label them based on fine-grained disinformation categories. We have curated a dataset of 40K tweets, annotated with both coarse and fine-grained labels. Following this, we designed models to predict the likelihood of tweets being deleted and to identify the potential reasons for their deletion. Our experiments, conducted using a variety of classic and transformer models, indicate that performance surpasses the majority baseline (e.g., 25% absolute improvement for fine-grained labels). We believe that such models can assist in moderating social media posts even before they are published

This Account Doesn’t Exist: Tweet Decay and the Politics of Deletion in the Brexit Debate

Literature on influence operations has identified metrics that are indicative of social media manipulation, but few studies have explored the lifecycle of low-quality information. We contribute to this literature by reconstructing nearly 3 million messages posted by 1 million users in the last days of the Brexit referendum campaign. While previous studies have found that on average only 4% of tweets disappear, we found that 33% of the tweets leading up to the referendum vote are no longer available. Only about half of the most active accounts that tweeted the referendum continue to operate publicly, and 20% of all accounts are no longer active. We tested whether partisan content was more likely to disappear and found more messages from the Leave campaign that disappeared than the entire universe of tweets affiliated with the Remain campaign. We compare these results with an assorted set of 45 hashtags posted in the same period and find that political campaigns present much higher ratios of user and tweet decay. These results are validated by inspecting 2 million Brexit-related tweets posted over a period of nearly 4 years. The article concludes with an overview of these findings and recommendations for future research

Twitter Discourse on the Pre-Presidential Election Campaign in Nigeria

Citizens’ political participation and engagement on various social media handles have made it necessary for scholars to investigate and understand the potentials inherent in the political engagement and discourse of individual citizens. Hence, the study examined Twitter discourse on the 2019 pre-presidential election campaign in Nigeria. Purposive sampling technique with thematic textual research method was used to thematically analyse tweets based on the research questions. Findings from the study showed that the kind of engagement made or done by Nigerians regarding the 2019 pre-presidential election campaigns was based on the topic, with most topics being met with sarcasm. The sarcasm found in the tweets pointed to the way Nigerians react in a situation that they have no way of rectifying. Also, findings from the study showed that celebrity tweet gets more engagement compared to tweets made by unpopular tweeps. Conclusively, the study found that the level of discourse on Twitter regarding Nigeria’s 2019 pre-presidential elections was very rich and participatory this implies that Nigerians have a high propensity to relate on social media as their rate of responses as well as their frequency of responses remained high throughout the election campaign period which to a large extent predicts real-life events

Diplomacy 2.0: The Future of Social Media in Nation Branding

The importance of social media as a tool of public diplomacy has gained traction in U.S. foreign policy initiatives. The Obama administration’s creation of “Diplomacy 2.0” has brought the use of Twitter and other social media sites to the front line of public diplomacy practices. This paper looks at why social media are an effective tool for two-way communication and how it can enhance U.S. public diplomacy initiatives. The author examines case studies of successful implementation of Twitter diplomacy and the use of Twitter for crisis management. Finally, the author concludes and discusses policy prescriptions, including Twitter implementation, relevant to the U.S. Department of State

Towards the Understanding of Private Content – Content-based Privacy Assessment and Protection in Social Networks

In the wake of the Facebook data breach scandal, users begin to realize how vulnerable their per-sonal data is and how blindly they trust the online social networks (OSNs) by giving them an inordinate amount of private data that touch on unlimited areas of their lives. In particular, stud-ies show that users sometimes reveal too much information or unintentionally release regretful messages, especially when they are careless, emotional, or unaware of privacy risks. Additionally, friends on social media platforms are also found to be adversarial and may leak one’s private in-formation. Threats from within users’ friend networks – insider threats by human or bots – may be more concerning because they are much less likely to be mitigated through existing solutions, e.g., the use of privacy settings. Therefore, we argue that the key component of privacy protection in social networks is protecting sensitive/private content, i.e. privacy as having the ability to control dissemination of information. A mechanism to automatically identify potentially sensitive/private posts and alert users before they are posted is urgently needed. In this dissertation, we propose a context-aware, text-based quantitative model for private in-formation assessment, namely PrivScore, which is expected to serve as the foundation of a privacy leakage alerting mechanism. We first explicitly research and study topics that might contain private content. Based on this knowledge, we solicit diverse opinions on the sensitiveness of private infor-mation from crowdsourcing workers, and examine the responses to discover a perceptual model behind the consensuses and disagreements. We then develop a computational scheme using deep neural networks to compute a context-free PrivScore (i.e., the “consensus” privacy score among average users). Finally, we integrate tweet histories, topic preferences and social contexts to gener-ate a personalized context-aware PrivScore. This privacy scoring mechanism could be employed to identify potentially-private messages and alert users to think again before posting them to OSNs. It could also benefit non-human users such as social media chatbots