Search Results for « Reddit Legends »

In this paper we introduce Reddit legends, a collection of venerated old posts that have become famous on Reddit. To establish the utility of Reddit legends for both computational science/HCI and folkloristics, we investigate two main questions: (1) whether they can be considered folklore, i.e. if they have consistent form, cultural significance, and undergo spontaneous transmission, and (2) whether they can be studied in a systematic manner. Through several subtasks, including the creation of a typology, an analysis of references to Reddit legends, and an examination of some of the textual characteristics of referencing behaviour, we show that Reddit legends can indeed be considered as folklore and that they are amendable to systematic text-based approaches. We discuss how these results will enable future analyses of folklore on Reddit, including tracking subreddit-wide and individual-user behaviour, and the relationship of this behaviour to other cultural markers.

The current research examines two similarly situated journalism organizations, The San Francisco Chronicle and San Francisco Gate , to compare how they are utilizing the social network Reddit. Through the lens of post-publication gatekeeping theory, findings demonstrate that the Chronicle 's approach is more effective in terms of generating higher karma scores and more comments despite posting less frequently. One of the biggest differences was that SFGate tended to post more soft news, which was less well-received. Other variables were examined to understand how the audience responded to the content regardless of which publication posted it. By highlighting the relationship between content type, audience engagement, and platform features, this study offers key takeaways for journalism organizations seeking to optimize their Reddit strategies. Practical and theoretical implications are discussed.

Information about gender differences in interests is necessary to disentangle the effects of discrimination and choice when gender inequalities occur, such as in employment. This article assesses gender differences in interests within the popular social news and entertainment site Reddit. A method to detect terms that are statistically significantly used more by males or females in 181 million comments in 100 subreddits shows that gender affects both the selection of subreddits and activities within most of them. The method avoids the hidden gender biases of topic modelling for this task. Although the method reveals statistically significant gender differences in interests for topics that are extensively discussed on Reddit, it cannot give definitive causes, and imitation and sharing within the site mean that additional checking is needed to verify the results. Nevertheless, with care, Reddit can serve as a useful source of insights into gender differences in interests.

As the largest open social medium on the Internet, Reddit is widely studied in the scientific literature. Due to its structured form and division into topical subfora (subreddits), conducted research often concerns connections and interactions between users and/or whole, subreddit-structure-based communities. Overall, the relations between communities are most often studied by applying graph networks, with various creation algorithms. In this work, a novel approach is proposed to build and understand the structure of Reddit. It is based on crossposts—posts that appeared on one subreddit and then were crossposted to another. After capturing one year of crossposts, a directed weighted graph network, using seven million posts from over 10,000 of the most popular subreddits, has been created. Using graph network algorithms, its characteristics are captured and compared to similar studies. We identify the information “sinks” and “sources”—the most active crossposting subreddits. Moreover, we obtained graph network metrics: the degree (modeled with the Power Law), clustering, community detection algorithms, and connected components structure network are compared to previous studies on Reddit network(s), yielding consistent, but also novel results. Finally, the relations between extensively studied subreddits (e.g., r/AITA, r/Parenting, r/politics) and new ones, which were not accounted for in previous research, opening new paths for data-driven studies, are summarized.

This paper compares Machine Learning and LSTM-based Deep Learning methods for sentiment analysis of Mobile Legends app reviews. Using a dataset of 10,000 reviews labeled as positive, negative, and neutral, the study evaluates traditional models with TF-IDF and PyCaret AutoML and compares them against an LSTM model designed to capture sequential text dependencies. The results show that the LSTM model outperforms the classical Machine Learning baselines, achieving 92% accuracy and a weighted F1-score of 91%. The findings indicate that deep learning is more effective for handling informal and context-dependent user review text.

Memes have been a media of entertainment when the internet has been a part of human’s life. Reddit as a source of a community who shares anything from news, information, gossip, to memes, is the core of this research. Fifty memes taken from a subreddit called r/HistoryMemes became the object of the research. The question is, do the memes exclusively enjoyed by the community or do the memes can also be brought outside the community and people still understand the joke? Using the Implicature theory, the researcher tries to answer the question. The result is that there is a possibility for the people outside the community of r/HistoryMemes subreddit to understand the joke that is delivered by the meme

We propose AutoLegend to generate interactive visualization legends using online learning with user feedback. AutoLegend accurately extracts symbols and channels from visualizations and then generates quality legends. AutoLegend enables a two-way interaction between legends and interactions, including highlighting, filtering, data retrieval, and retargeting. After analyzing visualization legends from IEEE VIS papers over the past 20 years, we summarized the design space and evaluation metrics for legend design in visualizations, particularly charts. The generation process consists of three interrelated components: a legend search agent, a feedback model, and an adversarial loss model. The search agent determines suitable legend solutions by exploring the design space and receives guidance from the feedback model through scalar scores. The feedback model is continuously updated by the adversarial loss model based on user input. The user study revealed that AutoLegend can learn users' preferences through legend editing.

Urban legends are a genre of modern folklore, consisting of stories about rare and exceptional events, just plausible enough to be believed, which tend to propagate inexorably across communities. In our view, while urban legends represent a form of "sticky" deceptive text, they are marked by a tension between the credible and incredible. They should be credible like a news article and incredible like a fairy tale to go viral. In particular we will focus on the idea that urban legends should mimic the details of news (who, where, when) to be credible, while they should be emotional and readable like a fairy tale to be catchy and memorable. Using NLP tools we will provide a quantitative analysis of these prototypical characteristics. We also lay out some machine learning experiments showing that it is possible to recognize an urban legend using just these simple features.

Este artigo, fundamentado no Modelo Dialogal de Plantin (2008), descreve os papéis actanciais e a dinâmica retórico-interacional da argumentação em ambientes digitais. Para tanto, adota as reflexões de Grácio (2011; 2013) sobre a situação argumentativa, os postulados de Marcuschi (2002) acerca dos gêneros digitais, os estudos de Kleiman (2005) e Coscarelli (2005) sobre letramentos digitais, bem como os pressupostos de Minozzo (2014) sobre o Reddit. Metodologicamente, toma como corpus um fórum on-line da plataforma com o tema "Liberdade de expressão não deve ser absoluta" e identifica os papéis actanciais — Proponente, Oponente e Terceiro — e os argumentos centrais desenvolvidos. Os resultados revelam a interlocução e atuação desses papéis, evidenciando que a argumentação se constitui como resposta à resistência do outro, exigindo justificativas mútuas. O anonimato e a estrutura de réplicas do Reddit fomentam o debate, mas também impõem a necessidade de sustentação argumentativa para evitar downvotes. Por fim, o estudo ressalta a importância do letramento digital para uma atuação crítica e ética em ambientes virtuais.