121 research outputs found
Two Species Evolutionary Game Model of User and Moderator Dynamics
We construct a two species evolutionary game model of an online society
consisting of ordinary users and behavior enforcers (moderators). Among
themselves, moderators play a coordination game choosing between being
"positive" or "negative" (or harsh) while ordinary users play prisoner's
dilemma. When interacting, moderators motivate good behavior (cooperation)
among the users through punitive actions while the moderators themselves are
encouraged or discouraged in their strategic choice by these interactions. We
show the following results: (i) We show that the -limit set of the
proposed system is sensitive both to the degree of punishment and the
proportion of moderators in closed form. (ii) We demonstrate that the basin of
attraction for the Pareto optimal strategy
can be computed exactly. (iii) We demonstrate that for certain initial
conditions the system is self-regulating. These results partially explain the
stability of many online users communities such as Reddit. We illustrate our
results with examples from this online system.Comment: 8 pages, 4 figures, submitted to 2012 ASE Conference on Social
Informatic
Lived experiences of online harm amongst marginalized and vulnerable individuals in support-seeking communities on Reddit
Online communities can serve as meaningful sources of social support,
particularly for marginalized and vulnerable groups. Disclosure of personal
information facilitates integration into online communities but may also expose
individuals to harm, including cyberbullying and manipulation. To better
understand negative user experiences resulting from self-disclosure in online
conversations, we interviewed 25 participants from target populations on
Reddit. Through thematic analysis, we outline the harm they experience,
including damage to self- and group identities. We find that encountering
online harm can worsen offline adversity. We discuss how users protect
themselves and recover from harm in the context of current platform
affordances, highlighting ongoing challenges. Finally, we explore design
implications for a community-driven, bottom-up approach to enhance user
well-being and safety
Preventing Information Leakage from Indexing in the Cloud
Cloud computing enables highly scalable services to be easily consumed over the Internet on an as-needed basis. While cloud computing is expanding rapidly and used by many individuals and organizations internationally, data protection issues in the cloud have not been carefully addressed at current stage. Users\u27 fear of confidential data (particularly financial and health data) leakage and loss of privacy in the cloud may become a significant barrier to the wide adoption of cloud services. in this paper, we explore a newly emerging problem of information leakage caused by indexing in the cloud. We design a three-tier data protection architecture to accommodate various levels of privacy concerns by users. According to the architecture, we develop a novel portable data binding technique to ensure strong enforcement of users\u27 privacy requirements at server side. © 2010 IEEE
ACCORD: Constraint-driven Mediation of Multi-user Conflicts in Cloud Services
When multiple users adopt collaborative cloud services like Google Drive to work on a shared resource, incorrect or missing permis- sions may cause conflicting or inconsistent access or use privileges. These issues (or conflicts) compromise resources confidentiality, integrity, or availability leading to a lack of trust in cloud services. An example conflict is when a user with editor permissions changes the permissions on a shared resource without consent from the orig- inal resource owner. In this demonstration, we introduce ACCORD, a web application built on top of Google Drive able to detect and resolve multi-user conflicts. ACCORD employs a simulator to help users preemptively identify potential conflicts and assists them in defining action constraints. Using these constraints, ACCORD can automatically detect and resolve any future conflicts
Sentiment analysis during Hurricane Sandy in emergency response
Sentiment analysis has been widely researched in the domain of online review sites with the aim of generating summarized opinions of users about different aspects of products. However, there has been little work focusing on identifying the polarity of sentiments expressed by users during disaster events. Identifying such sentiments from online social networking sites can help emergency responders understand the dynamics of the network, e.g., the main users' concerns, panics, and the emotional impacts of interactions among members. In this paper, we perform a sentiment analysis of tweets posted on Twitter during the disastrous Hurricane Sandy and visualize online users' sentiments on a geographical map centered around the hurricane. We show how users' sentiments change according not only to their locations, but also based on the distance from the disaster. In addition, we study how the divergence of sentiments in a tweet posted during the hurricane affects the tweet retweetability. We find that extracting sentiments during a disaster may help emergency responders develop stronger situational awareness of the disaster zone itself
Understanding and Predicting Retractions of Published Work
Recent increases in the number of retractions of published papers reflect heightened attention and increased scrutiny in the scientific process motivated, in part, by the replication crisis. These trends motivate computational tools for understanding and assessment of the scholarly record. Here, we sketch the landscape of retracted papers in the Retraction Watch database, a collection of 19k records of published scholarly articles that have been retracted for various reasons (e.g., plagiarism, data error). Using metadata as well as features derived from full-text for a subset of retracted papers in the social and behavioral sciences, we develop a random forest classifier to predict retraction in new samples with 73% accuracy and F1-score of 71%. We believe this study to be the first of its kind to demonstrate the utility of machine learning as a tool for the assessment of retracted work
Tweet Factors Influencing Trust and Usefulness During Both Man-Made and Natural Disasters
ABSTRACT To this date, research on crisis informatics has focused on the detection of trust in Twitter data through the use of message structure, sentiment, propagation and author. Little research has examined the usefulness of these messages in the crisis response domain. Toward detecting useful messages in case of crisis, in this paper, we characterize tweets, which are perceived useful or trustworthy, and determine their main features. Our analysis is carried out on two datasets (one natural and one man made) gathered from Twitter concerning hurricane Sandy in 2012 and the Boston Bombing 2013. The results indicate that there is a high correlation and similar factors (support for the victims, informational data, use of humor and type of emotion used) influencing trustworthiness and usefulness for both disaster types. This could have impacts on how messages from social media data are analyzed for use in crisis response
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