A Clustering Algorithm for Early Prediction of Controversial Reddit Posts

Social curation platforms like Reddit are rich with user interactions such as comments, upvotes, and downvotes. Predicting these interactions before they happen is an interesting computational challenge and can be used for a variety of tasks, ranging from content moderation to personality prediction. Given the vast amount of information posted on these sites, it\u27s important to develop models that can simplify this prediction task. In this paper, we present a simple clustering algorithm that helps predict the controversiality of a Reddit post using the user\u27s profile information, their past contributions on Reddit, and the sentiment expressed in their post. On average, introducing the cluster to the prediction task improved the accuracy of the prediction by over 20 percent, with F1 scores of 0.95 (micro) and 0.7 (macro). The classifier performs better than a majority predictor. The results also show that the overwhelming majority of users are inactive and when they do post, they post non-controversial content

Identifying Experts in Question \& Answer Portals: A Case Study on Data Science Competencies in Reddit

The irreplaceable key to the triumph of Question & Answer (Q&A) platforms is their users providing high-quality answers to the challenging questions posted across various topics of interest. Recently, the expert finding problem attracted much attention in information retrieval research. In this work, we inspect the feasibility of supervised learning model to identify data science experts in Reddit. Our method is based on the manual coding results where two data science experts labelled expert, non-expert and out-of-scope comments. We present a semi-supervised approach using the activity behaviour of every user, including Natural Language Processing (NLP), crowdsourced and user feature sets. We conclude that the NLP and user feature sets contribute the most to the better identification of these three classes It means that this method can generalise well within the domain. Moreover, we present different types of users, which can be helpful to detect various types of users in the future

Identifying Professional Photographers Through Image Quality and Aesthetics in Flickr

In our generation, there is an undoubted rise in the use of social media and specifically photo and video sharing platforms. These sites have proved their ability to yield rich data sets through the users' interaction which can be used to perform a data-driven evaluation of capabilities. Nevertheless, this study reveals the lack of suitable data sets in photo and video sharing platforms and evaluation processes across them. In this way, our first contribution is the creation of one of the largest labelled data sets in Flickr with the multimodal data which has been open sourced as part of this contribution. Predicated on these data, we explored machine learning models and concluded that it is feasible to properly predict whether a user is a professional photographer or not based on self-reported occupation labels and several feature representations out of the user, photo and crowdsourced sets. We also examined the relationship between the aesthetics and technical quality of a picture and the social activity of that picture. Finally, we depicted which characteristics differentiate professional photographers from non-professionals. As far as we know, the results presented in this work represent an important novelty for the users' expertise identification which researchers from various domains can use for different applications

Characterization of Time-variant and Time-invariant Assessment of Suicidality on Reddit using C-SSRS

Suicide is the 10th leading cause of death in the U.S (1999-2019). However, predicting when someone will attempt suicide has been nearly impossible. In the modern world, many individuals suffering from mental illness seek emotional support and advice on well-known and easily-accessible social media platforms such as Reddit. While prior artificial intelligence research has demonstrated the ability to extract valuable information from social media on suicidal thoughts and behaviors, these efforts have not considered both severity and temporality of risk. The insights made possible by access to such data have enormous clinical potential - most dramatically envisioned as a trigger to employ timely and targeted interventions (i.e., voluntary and involuntary psychiatric hospitalization) to save lives. In this work, we address this knowledge gap by developing deep learning algorithms to assess suicide risk in terms of severity and temporality from Reddit data based on the Columbia Suicide Severity Rating Scale (C-SSRS). In particular, we employ two deep learning approaches: time-variant and time-invariant modeling, for user-level suicide risk assessment, and evaluate their performance against a clinician-adjudicated gold standard Reddit corpus annotated based on the C-SSRS. Our results suggest that the time-variant approach outperforms the time-invariant method in the assessment of suicide-related ideations and supportive behaviors (AUC:0.78), while the time-invariant model performed better in predicting suicide-related behaviors and suicide attempt (AUC:0.64). The proposed approach can be integrated with clinical diagnostic interviews for improving suicide risk assessments.Comment: 24 Pages, 8 Tables, 6 Figures; Accepted by PLoS One ; One of the two mentioned Datasets in the manuscript has Closed Access. We will make it public after PLoS One produces the manuscrip

Networks and trust: systems for understanding and supporting internet security

Includes bibliographical references.2022 Fall.This dissertation takes a systems-level view of the multitude of existing trust management systems to make sense of when, where and how (or, in some cases, if) each is best utilized. Trust is a belief by one person that by transacting with another person (or organization) within a specific context, a positive outcome will result. Trust serves as a heuristic that enables us to simplify the dozens decisions we make each day about whom we will transact with. In today's hyperconnected world, in which for many people a bulk of their daily transactions related to business, entertainment, news, and even critical services like healthcare take place online, we tend to rely even more on heuristics like trust to help us simplify complex decisions. Thus, trust plays a critical role in online transactions. For this reason, over the past several decades researchers have developed a plethora of trust metrics and trust management systems for use in online systems. These systems have been most frequently applied to improve recommender systems and reputation systems. They have been designed for and applied to varied online systems including peer-to-peer (P2P) filesharing networks, e-commerce platforms, online social networks, messaging and communication networks, sensor networks, distributed computing networks, and others. However, comparatively little research has examined the effects on individuals, organizations or society of the presence or absence of trust in online sociotechnical systems. Using these existing trust metrics and trust management systems, we design a set of experiments to benchmark the performance of these existing systems, which rely heavily on network analysis methods. Drawing on the experiments' results, we propose a heuristic decision-making framework for selecting a trust management system for use in online systems. In this dissertation we also investigate several related but distinct aspects of trust in online sociotechnical systems. Using network/graph analysis methods, we examine how trust (or lack of trust) affects the performance of online networks in terms of security and quality of service. We explore the structure and behavior of online networks including Twitter, GitHub, and Reddit through the lens of trust. We find that higher levels of trust within a network are associated with more spread of misinformation (a form of cybersecurity threat, according to the US CISA) on Twitter. We also find that higher levels of trust in open source developer networks on GitHub are associated with more frequent incidences of cybersecurity vulnerabilities. Using our experimental and empirical findings previously described, we apply the Systems Engineering Process to design and prototype a trust management tool for use on Reddit, which we dub Coni the Trust Moderating Bot. Coni is, to the best of our knowledge, the first trust management tool designed specifically for use on the Reddit platform. Through our work with Coni, we develop and present a blueprint for constructing a Reddit trust tool which not only measures trust levels, but can use these trust levels to take actions on Reddit to improve the quality of submissions within the community (a subreddit)