Social media systems allow Internet users a congenial platform to freely express their thoughts and opinions. Although this property represents incredible and unique communication opportunities, it also brings along important challenges. Online hate speech is an archetypal example of such challenges. Despite its magnitude and scale, there is a significant gap in understanding the nature of hate speech on social media. In this paper, we provide the first of a kind systematic large scale measurement study of the main targets of hate speech in online social media. To do that, we gather traces from two social media systems: Whisper and Twitter. We then develop and validate a methodology to identify hate speech on both these systems. Our results identify online hate speech forms and offer a broader understanding of the phenomenon, providing directions for prevention and detection approaches.
Issue tracking systems such as Bugzilla are tools to facilitate collaboration between software maintenance professionals. Popular issue tracking systems consists of discussion forums to facilitate bug reporting and comment posting. We observe that several comments posted in issue tracking system contains link to external websites such as YouTube (video sharing website), Twitter (micro-blogging website), Stackoverflow (a community-based question and answering website for programmers), Wikipedia and focused discussions forums. Stackoverflow is a popular community-based question and answering website for programmers and is widely used by software engineers as it contains answers to millions of questions (an extensive knowledge resource) posted by programmers on diverse topics. We conduct a series of experiments on open-source Google Chromium and Android issue tracker data (publicly available real-world dataset) to understand the role and impact of Stackoverflow in issue resolution. Our experimental results show evidences of several references to Stackoverflow in threaded discussions and demonstrate correlation between a lower mean time to repair (in one dataset) with presence of Stackoverflow links. We also observe that the average number of comments posted in response to bug reports are less when Stackoverflow links are presented in contrast to bug reports not containing Stackoverflow references. We conduct experiments based on textual similarly analysis (content-based linguistic features) and contextual data analysis (exploited metadata such as tags associated to a Stackoverflow question) to recommend Stackoverflow questions for an incoming bug report. We perform empirical analysis to measure the effectiveness of the proposed method on a dataset containing ground-truth and present our insights. We present the result of a survey (of Google Chromium Developers) that we conducted to understand practitioner's perspective and experience. I. RESEARCH MOTIVATION AND AIMResearch shows that software developers spend a significant amount of their time outside their development environment (such as Eclipse Integrated Development Environment) and inside their Web Browser searching and navigating information available on the Web required for problem-solving [8]. Brandt et al. investigate the role of online information and resources while programming [5]. Their case-study reveals that programmers spent 19% of their programming time on the Web to accomplish several different kinds of activities (such as learning of unfamiliar concepts, language syntax, API or function usage, reading tutorials or how-to articles, connecting high-level knowledge to implementation details) [5]. Communication, collaboration, exchanging knowledge and searching for information is not only a common activity during development but also during bug resolution and bug fixing [3][13]. Bertram et al. mention that issue tracking is a social process and issue tracking systems are a focal point for communication and coordination between stakeholders [3].We obse...
Recently, there has been a significant increase in the popularity of anonymous social media sites like Whisper and Secret. Unlike traditional social media sites like Facebook and Twitter, posts on anonymous social media sites are not associated with well defined user identities or profiles. In this study, our goals are two-fold: (i) to understand the nature (sensitivity, types) of content posted on anonymous social media sites and (ii) to investigate the differences between content posted on anonymous and non-anonymous social me- dia sites like Twitter. To this end, we gather and analyze ex- tensive content traces from Whisper (anonymous) and Twitter (non-anonymous) social media sites. We introduce the notion of anonymity sensitivity of a social media post, which captures the extent to which users think the post should be anonymous. We also propose a human annotator based methodology to measure the same for Whisper and Twitter posts. Our analysis reveals that anonymity sensitivity of most whispers (unlike tweets) is not binary. Instead, most whispers exhibit many shades or different levels of anonymity. We also find that the linguistic differences between whispers and tweets are so significant that we could train automated classifiers to distinguish between them with reasonable accuracy. Our findings shed light on human behavior in anonymous media systems that lack the notion of an identity and they have important implications for the future designs of such systems.
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