Reddit Comments Research Articles

Charting closed-loop collective cultural decisions: from book best sellers and music downloads to Twitter hashtags and Reddit comments

Charts are used to measure relative success for a large variety of cultural items. Traditional music charts have been shown to follow self-organizing principles with regard to the distribution of item lifetimes, the on-chart residence times. Here we examine if this observation holds also for (a) music streaming charts (b) book best-seller lists and (c) for social network activity charts, such as Twitter hashtags and the number of comments Reddit postings receive. We find that charts based on the active production of items, like commenting, are more likely to be influenced by external factors, in particular by the 24 h day–night cycle. External factors are less important for consumption-based charts (sales, downloads), which can be explained by a generic theory of decision-making. In this view, humans aim to optimize the information content of the internal representation of the outside world, which is logarithmically compressed. Further support for information maximization is argued to arise from the comparison of hourly, daily and weekly charts, which allow to gauge the importance of decision times with respect to the chart compilation period.

RedBERT

A natural language processing (NLP) method was used to uncover various issues and sentiments surrounding COVID-19 from social media and get a deeper understanding of fluctuating public opinion in situations of wide-scale panic to guide improved decision making with the help of a sentiment analyser created for the automated extraction of COVID-19-related discussions based on topic modelling. Moreover, the BERT model was used for the sentiment classification of COVID-19 Reddit comments. These findings shed light on the importance of studying trends and using computational techniques to assess the human psyche in times of distress.

Pragmatic extension in computer-mediated communication: The case of ‘#’ and ‘™’

There is a growing body of work discussing the evolving functions of computer-mediated communication (CMC) cues including emoticons, emojis, memes, punctuation and GIFs. Two such cues are the hashtag (#) and the trademark operator (™). Using a corpus of public Reddit comments from 10 distinct ‘subreddits’, I analyze the usage of the trademark operator across different online communities. I demonstrate that the trademark operator use is undergoing a shift to take on characteristics previously identified by Zappavigna (2018) to correspond with the hashtag. In addition to having shared metadiscursive properties, these operators have overlapping functions including construing evaluation, ambient affiliation, and critique. One possible motivation for the use of these symbols is the indexing of community membership, since the use of these contextually defined operators assumes a degree of shared common ground. I argue that the overlapping characteristics extend to a broader class of CMC cues that I refer to as artificial operators.

The role of digital health technologies in COVID-19 surveillance and recovery: a specific case of long haulers

Digital health and technologies are essential to curbing the novel coronavirus 2019 (COVID-19) pandemic especially with shelter-in-place and social distancing orders. Epidemiologists and public health officials are tapping into frequently used technologies like wearables, digital devices, digital and social media data to detect and validate COVID-19 symptoms throughout the pandemic, especially during early stages when symptoms were evolving. In this article, we review how digital technologies and social media platforms can identify and inform our understanding of COVID-19 pandemic surveillance and recovery efforts. We analyze Reddit narrative posts and comments on r/covidlonghaulers to demonstrate how social media can be used to better understand COVID-19 pandemic. Using Reddit data, we highlight long haulers' patient journeys and shed light on potential consequences of their condition. We identified 21 themes, of which the following were significantly associated with valence: COVID-19 Symptoms (r = −0.037), medical advice (r = −0.030), medical system (r = −0.029), bodily processes (r = −0.020), questions (r = 0.024), physical activity (r = 0.033), self-differentials and negations (r = 0.040) and supplements (r = 0.025). Our brief literature review and analysis of r/covidlonghaulers narrative posts demonstrate the value of digital technologies and social media platforms as they act as modern avenues for public health, safety, and well-being.

Detecting the target of sarcasm is hard: Really??

Sarcasm target detection (identifying the target of mockery in a sarcastic sentence) is an emerging field in computational linguistics. Although there has been some research in this field, accurately identifying the target still remains problematic especially when the target of mockery is not presented in the text. In this paper, we propose a combination of a machine learning classifier and a deep learning model to extract the target of sarcasm from the text. First, we classify sarcastic sentences using machine learning, to determine whether a sarcastic sentence contains a target. Then we use a deep learning model from Aspect-Based Sentiment Analysis to extract the target. Our proposed system is evaluated on three publicly available data sets: sarcastic book snippets, sarcastic tweets, and sarcastic Reddit comments. Our evaluation results show that our approach achieves equal or better performance compared to the current state-of-the-art system, with an 18% improvement on the Reddit data set and similar scores on the Books and Tweets data sets. This is because our method is able to accurately identify when the target of sarcasm is not present. The primary challenge we identify, that is hindering the creation of a high accuracy classifier, is the lack of consistency among human annotators in identifying the target of sarcasm within standard ground-truth data sets.

Stressed? Depressed? You are not alone

Many physicists find that establishing work–life balance is crucial to battling the COVID-related stresses of isolation, low productivity, and despondency.

What Makes People Join Conspiracy Communities?

Widespread conspiracy theories, like those motivating anti-vaccination attitudes or climate change denial, propel collective action, and bear society-wide consequences. Yet, empirical research has largely studied conspiracy theory adoption as an individual pursuit, rather than as a socially mediated process. What makes users join communities endorsing and spreading conspiracy theories? We leverage longitudinal data from 56 conspiracy communities on Reddit to compare individual and social factors determining which users join the communities. Using a quasi-experimental approach, we first identify 30K future conspiracists?(FC) and30K matched non-conspiracists?(NC). We then provide empirical evidence of the importance of social factors across six dimensions relative to the individual factors by analyzing 6 million Reddit comments and posts. Specifically, in social factors, we find that dyadic interactions with members of the conspiracy communities and marginalization outside of the conspiracy communities are the most important social precursors to conspiracy joining-even outperforming individual factor baselines. Our results offer quantitative backing to understand social processes and echo chamber effects in conspiratorial engagement, with important implications for democratic institutions and online communities.

Boosting Cryptocurrency Return Prediction

We use boosted decision trees to generate daily out-of-sample forecasts of excess returns for Bitcoin and Ethereum, the two best-known and largest cryptocurrencies. The decision trees incorporate information from 39 predictors, including variables relating to cryptocurrency fundamentals, technical indicators, Google Trends searches, Reddit comments, and articles from Factiva. We use the XGBoost algorithm to boost trees and find that excess return forecasts based on boosted trees produce statistically and economically significant out-of-sample gains. We explore the importance of individual predictors and nonlinearities in the fitted boosted trees. We find that a broad array of predictors are relevant for forecasting daily cryptocurrency returns and that strong nonlinearities characterize the predictive relationships.

Characterizing Discourse about COVID-19 Vaccines: A Reddit Version of the Pandemic Story.

It has been one year since the outbreak of the COVID-19 pandemic. The good news is that vaccines developed by several manufacturers are being actively distributed worldwide. However, as more and more vaccines become available to the public, various concerns related to vaccines become the primary barriers that may hinder the public from getting vaccinated. Considering the complexities of these concerns and their potential hazards, this study is aimed at offering a clear understanding about different population groups' underlying concerns when they talk about COVID-19 vaccines-particularly those active on Reddit. The goal is achieved by applying LDA and LIWC to characterize the pertaining discourse with insights generated through a combination of quantitative and qualitative comparisons. Findings include the following: (1) during the pandemic, the proportion of Reddit comments predominated by conspiracy theories outweighed that of any other topics; (2) each subreddit has its own user bases, so information posted in one subreddit may not reach that from other subreddits; and (3) since users' concerns vary across time and subreddits, communication strategies must be adjusted according to specific needs. The results of this study manifest challenges as well as opportunities in the process of designing effective communication and immunization programs.

“This show hits really close to home on so many levels”: An analysis of Reddit comments about HBO’s Euphoria to understand viewers’ experiences of and reactions to substance use and mental illness

BackgroundPublic health has begun using social media forums such as Reddit to enhance surveillance and modernize interventions for young people. The current study’s objective was to examine Reddit posts about the HBO series Euphoria to identify show themes that resonate with adolescent and young adult viewers in order to inform future social media interventions. MethodsReddit comments in the r/television community from June to August 2019 were downloaded. Following filtering, 725 comments were analyzed and coded using a codebook and ATLAS.ti. Coded comments were analyzed for themes relevant to Redditor substance use, reactions to Euphoria and the main character (Rue), and mental health concerns. ResultsDuring their discussion of the show, Redditors disclosed both personal recreational and prescription drug use, including substance use to cope with mental illness symptoms. There were approximately equal numbers of comments with positive and negative reactions to the show overall and to the main character, Rue. Redditors often found Euphoria’s storyline and portrayed events to be relatable and realistic to the experience of young people who use drugs, as well as sometimes triggering. Overall, Redditors thought Rue accurately depicted an individual’s struggle with a substance use disorder. ConclusionsThis exploratory study highlights how television and social media can contribute to young peoples’ understanding of substance use disorders and mental health. Findings could inform the design of social media interventions for adolescents and young adults on a variety of substance use issues, including stigma and the interconnectedness of substance use and mental health challenges.

Exploring Antecedents and Consequences of Toxicity in Online Discussions

Toxicity in online discussions has been an intriguing phenomenon and an important problem. In this paper, we seek to better understand toxicity dynamics in online discussions via a case study on Reddit that explores the antecedents and consequences of toxicity in text. We inspected two dimensions of toxicity: language toxicity, i.e. how toxic the text itself is; and toxicity elicitation, i.e. how much toxicity it elicits in its response. Through regression analyses on Reddit comments, we found that both author propensity and toxicity in discussion context were strong positive antecedents of language toxicity; meanwhile, language toxicity significantly increased the volume and user evaluation of the discussion in some sub-communities, while toxicity elicitation showed mixed effects. We then discuss how our results help understand and regulate toxicity in online discussions by interpreting the complicated triggers and outcomes of toxicity.

A Hierarchy of Acceptable Changes for Audiences of Nostalgic Narrative Media Brands

ABSTRACT This research used the branding concepts of brand nostalgia and brand heritage to examine and understand the changes that audiences will accept to a narrative media brand, a term proposed by this research to help distinguish between different types of media brands with the aim of focusing and advancing media branding research into fictional media. Narrative media brands involve a continuing fictional narrative such as the Pokémon brand chosen for this research. The context was Reddit comments posted about three trailers for the ‘I Choose You’ movie before its 2017 release to celebrate 20 years of the franchise. 574 Reddit posts were downloaded and analyzed using Leximancer software and an adaptation of netnography. It was found that changes that respected the audiences’ parasocial relationships with the characters, as well as their perceptions of the brand’s heritage, were acceptable. However, changes that audiences believed would threaten and undermine these relationships and the brand’s heritage were not. This is presented in a pyramid hierarchy to conceptualize and categorize the level of change deemed acceptable by audiences. This research extends media branding research beyond seminal concepts by applying the concepts of brand nostalgia and brand heritage to a narrative media brand. The hierarchy has practical applications for the writers, producers, and brand managers of narrative media brands.

#BeTheMatch: Assessing How Testimonials on Reddit Promote the Importance of Donating Bone Marrow

This study explores how sharing testimonials on the social media network Reddit may encourage individuals to donate bone marrow. The theory of planned behavior guided a quantitative content analysis of 1,015 Reddit comments about donation. Research questions asked how individuals post about donation and how Redditors engage with this content. Overall, comments addressed more positive than negative outcome, efficacy, and normative beliefs. Comments that discussed beliefs related to registering to donate and the process of donating received significantly higher engagement than other comments. Additionally, comments that included positive outcome beliefs related to registering, positive efficacy beliefs related to registering and donating, and positive normative beliefs related to donating received higher engagement than other comments. Results suggest testimonials may be most effective if they focus on behavior facilitators and if they present donation as a positive norm deviant behavior. Finally, the Reddit forums functioned as places for individuals to seek out and share information. Comments included calls to action and hyperlinks for health resources. This research reveals how organic conversations on Reddit may promote health information seeking and advocacy behavior adoption. Implications for the theory of planned behavior and the use of Reddit as a platform for health promotion are discussed.

Lay perspectives on social distancing and other official recommendations and regulations in the time of COVID-19: a qualitative study of social media posts

BackgroundCOVID-19 caused by a new form of coronavirus (SARS-CoV-2) first appeared in China end of 2019 and quickly spread to all counties of the world. To slow down the spread of the virus and to limit the pressure on the health care systems, different regulations and recommendations have been implemented by authorities, comprising amongst others the closure of all entertainment venues and social distancing. These measures have received mixed reactions, particularly from young individuals, with many not following available advice. Drawing on the information in social media discussion forums, the present study explores the reasons why people ignore the orders and recommendations of the authorities and why the authorities are unable to produce a shared sense of inclusion concerning protective measures against the COVID-19 outbreak.MethodsThree open-access social media forums (Reddit, Twitter, and YouTube comments) were systematically searched with respect to COVID-19-related beliefs, attitudes, and behaviours of individuals. The data was retrieved in the first 3 weeks of March 2020. Qualitative document analysis and qualitative content analysis were used as the methodical approach. The data was reviewed by all authors and jointly interpreted to minimise inconsistencies.ResultsThe study reveals that reasons such as information pollution on social media, the persistence of uncertainty about the rapidly spreading virus, the impact of the social environment on the individual, and fear of unemployment associated with inequality in the distribution of income lead people to ignore the orders and recommendations of the authorities. The findings suggest that government representatives and politicians could not produce a shared sense of inclusion concerning protective measures against the COVID-19 outbreak, due to not building trust among the public and taking concrete economic steps to satisfy them.ConclusionIn uncertain crises, transparency in the presentation of information and government policies emerge as influential determinants in creating social susceptibility and solidarity. The differences between social classes constitute one of the important factors that affect the decision-making mechanisms of individuals in determining the necessary steps to be undertaken in times of crisis.

A pure meritocracy blind to identity: Exploring the Online Responses to All-Female Esports Teams in Reddit

Despite recent growth and popularity, esports as a scene is struggling with a number of problems ranging from payment problems and cheating to questionable treatment based on various factors such as race and gender. In this paper we seek to uncover how perceptions of women in esports are guided by stereotypes of all-female teams and ‘female professional players’. Our data consists of 952 Reddit comments on two announcements of all-female teams in League of Legends and Counter-Strike: Global Offensive. The nature of esports was perceived as a working meritocracy where only player skill matters. Especially all-female teams were seen to be a threat to this order, since they were considered to lack dedication and have ulterior motives for playing the game. Ultimately, getting to visibly exist as a woman in the scene was a reward for compliance in the esports meritocracy: exhibiting skill, playing in mixed teams, and tolerating harassment.

“That spelling tho”: A sociolinguistic study of the nonstandard form of though in a corpus of Reddit comments

AbstractTho, the nonstandard spelling of though which was proposed by American spelling reformers in the 19th century, is making a comeback. In 2013, internet memes such as that backflip tho gave a boost to the shortened form. This sociolinguistic study investigates the use of tho in RedditGender, a 19 million-word corpus of comments posted by 1044 Reddit users. First, concordance lines generated from the whole corpus were analyzed in order to compare the use of tho with the meme and the standard spelling. Then, regression analysis was conducted with a sample of the corpus, containing only complete cases. Results show that tho is rarely used in the meme construction that contributed to popularize it, and that it appears more often as an adverb than as a conjunction. They also seem to indicate that the use of tho is correlated with gender and race. Most frequent users are black males. This suggests that the shortened spelling is not simply a way to save time when typing, and that it is not semantically equivalent to though. It seems to be a marker of affiliation with a social group and of familiarity with internet subcultures.

Natural language processing of Reddit data to evaluate dermatology patient experiences and therapeutics

There is a lack of research studying patient-generated data on Reddit, one of the world's most popular forums with active users interested in dermatology. Techniques within natural language processing, a field of artificial intelligence, can analyze large amounts of text information and extract insights. To apply natural language processing to Reddit comments about dermatology topics to assess for feasibility and potential for insights and engagement. A software pipeline preprocessed Reddit comments from 2005 to 2017 from 7 popular dermatology-related subforums on Reddit, applied latent Dirichletallocation, and used spectral clustering to establish cohesive themes and the frequency of word representation and grouped terms within these topics. We created a corpus of 176,000 comments and identified trends in patient engagement in spaces such as eczema and acne, among others, with a focus on homeopathic treatments and isotretinoin. Latent Dirichletallocation is an unsupervised model, meaning there is no ground truth to which the model output can be compared. However, because these forums are anonymous, there seems little incentive for patients to be dishonest. Reddit data has viability and utility for dermatologic research and engagement with the public, especially for common dermatology topics such as tanning, acne, and psoriasis.

Prevalence and Psychological Effects of Hateful Speech in Online College Communities.

Hateful speech bears negative repercussions and is particularly damaging in college communities. The efforts to regulate hateful speech on college campuses pose vexing socio-political problems, and the interventions to mitigate the effects require evaluating the pervasiveness of the phenomenon on campuses as well the impacts on students' psychological state. Given the growing use of social media among college students, we target the above issues by studying the online aspect of hateful speech in a dataset of 6 million Reddit comments shared in 174 college communities. To quantify the prevelence of hateful speech in an online college community, we devise College Hate Index (CHX). Next, we examine its distribution across the categories of hateful speech, behavior, class, disability, ethnicity, gender, physical appearance, race, religion, and sexual orientation. We then employ a causal-inference framework to study the psychological effects of hateful speech, particularly in the form of individuals' online stress expression. Finally, we characterize their psychological endurance to hateful speech by analyzing their language- their discriminatory keyword use, and their personality traits. We find that hateful speech is prevalent in college subreddits, and 25% of them show greater hateful speech than non-college subreddits. We also find that the exposure to hate leads to greater stress expression. However, everybody exposed is not equally affected; some show lower psychological endurance than others. Low endurance individuals are more vulnerable to emotional outbursts, and are more neurotic than those with higher endurance. Our work bears implications for policy-making and intervention efforts to tackle the damaging effects of online hateful speech in colleges. From technological perspective, our work caters to mental health support provisions on college campuses, and to moderation efforts in online college communities. In addition, given the charged aspect of speech dilemma, we highlight the ethical implications of our work. Our work lays the foundation for studying the psychological impacts of hateful speech in online communities in general, and situated communities in particular (the ones that have both an offline and an online analog).

Characterizing Trends in Human Papillomavirus Vaccine Discourse on Reddit (2007-2015): An Observational Study.

BackgroundDespite the introduction of the human papillomavirus (HPV) vaccination as a preventive measure in 2006 for cervical and other cancers, uptake rates remain suboptimal, resulting in preventable cancer mortality. Social media, widely used for information seeking, can influence users’ knowledge and attitudes regarding HPV vaccination. Little is known regarding attitudes related to HPV vaccination on Reddit (a popular news aggregation site and online community), particularly related to cancer risk and sexual activity. Examining HPV vaccine–related messages on Reddit may provide insight into how HPV discussions are characterized on forums online and influence decision making related to vaccination.ObjectiveWe observed how the HPV vaccine is characterized on Reddit over time and by user gender. Specifically, this study aimed to determine (1) if Reddit messages are more related to cancer risks or sexual behavior and (2) what other HPV vaccine–related discussion topics appear on Reddit.MethodsWe gathered all public Reddit comments from January 2007 to September 2015. We manually annotated 400 messages to generate keywords and identify salient themes. We then measured the similarity between each comment and lists of keywords associated with sexual behavior and cancer risk using Latent Semantic Analysis (LSA). Next, we used Latent Dirichlet Allocation (LDA) to characterize remaining topics within the Reddit data.ResultsWe analyzed 22,729 messages containing the strings hpv or human papillomavirus and vaccin. LSA findings show that HPV vaccine discussions are significantly more related to cancer compared with sexual behavior from 2008 to 2015 (P<.001). We did not find a significant difference between genders in discussions of cancer and sexual activity (P>.05). LDA analyses demonstrated that although topics related to cancer risk and sexual activity were both frequently discussed (16.1% and 14.5% of word tokens, respectively), the majority of online discussions featured other topics. The most frequently discussed topic was politics associated with the vaccine (17.2%). Other topics included HPV disease and/or immunity (13.5%), the HPV vaccine schedule (11.5%), HPV vaccine side effects (9.7%), hyperlinks to outside sources (9.1%), and the risks and benefit of HPV vaccination (8.5%).ConclusionsReddit discourse on HPV vaccine encompasses a broad range of topics among men and women, with HPV political debates and cancer risk making up the plurality of the discussion. Our findings demonstrated that women and men both discussed HPV, highlighting that Reddit users do not perceive HPV as an issue that only pertains to women. Given the increasing use of social media as a source of health information, these results can inform the development of targeted online health communication strategies to promote HPV vaccination to young adult users of Reddit. Analyzing online discussions on Reddit can inform health communication efforts by identifying relevant, important HPV-related topics among online communities.

Where should one get news updates: Twitter or Reddit

The last decade witnessed an enormous growth in popularity of several social media platforms. Although these platforms are generally meant to share information and opinions, their ubiquity is being increasingly exploited for spreading news and events in real time. Hence these platforms have become a natural choice for news agencies for getting updates, comments and experts’ opinion of ongoing events which is crucial to understand the societal impact and for writing reports/editorial. However, with the plethora of these platforms available, each having its own uniqueness in content presentation, spreading patterns and also in the user interests, a comparative study of the efficacy of these platforms for different journalistic purposes would be useful.In this paper, we perform a comparative study of two leading social media platforms Reddit and Twitter. We have analyzed Reddit comments and Twitter feeds of six news categories to establish the efficacy of these platforms in terms of different journalistic requirements. Observations reveal that there exist significant differences across these platforms that can be suitably exploited depending upon the scope of the requirements; for example, while Twitter is a better choice for the evolutionary study of events, Reddit is the more natural choice for exploration during the initial phase of any event. While the availability and spread of updated information on Twitter can be key in emergency and disaster situations, critical analysis of posts in Reddit can be important for editorials.