Imrul Kayes

The Social World of Content Abusers in Community Question Answering

Community-based question answering platforms can be rich sources of information on a variety of specialized topics, from finance to cooking. The usefulness of such platforms depends heavily on user contributions (questions and answers), but also on respecting the community rules. As a crowd-sourced service, such platforms rely on their users for monitoring and flagging content that violates community rules. Common wisdom is to eliminate the users who receive many flags. Our analysis of a year of traces from a mature Q&A site shows that the number of flags does not tell the full story: on one hand, users with many flags may still contribute positively to the community. On the other hand, users who never get flagged are found to violate community rules and get their accounts suspended. This analysis, however, also shows that abusive users are betrayed by their network properties: we find strong evidence of homophilous behavior and use this finding to detect abusive users who go under the community radar. Based on our empirical observations, we build a classifier that is able to detect abusive users with an accuracy as high as 83%.

Privacy and security in online social networks: A survey

Abstract Online social networks (OSN) are a permanent presence in today’s personal and professional lives of a huge segment of the population, with direct consequences to offline activities. Built on a foundation of trust – users connect to other users with common interests or overlapping personal trajectories – online social networks and the associated applications extract an unprecedented volume of personal information. Unsurprisingly, serious privacy and security risks emerged, positioning themselves along two main types of attacks: attacks that exploit the implicit trust embedded in declared social relationships; and attacks that harvest user’s personal information for ill-intended use. This article provides an overview of the privacy and security issues that emerged so far in OSNs. We introduce a taxonomy of privacy and security attacks in OSNs, we overview existing solutions to mitigate those attacks, and outline challenges still to overcome.

How influential are you: detecting influential bloggers in a blogging community

Blogging is a popular activity with high impact on marketing, shaping public opinions, and informing the world about major events from a grassroots point of view. Influential bloggers are recognized by businesses as significant forces for product promotion or demotion, and by oppressive political regimes as serious threats to their power. This paper studies the problem of identifying influential bloggers in a blogging community, BlogCatalog, by using network centrality metrics. Our analysis shows that bloggers are connected in a core-periphery network structure, with the highly influential bloggers well connected with each others forming the core, and the non-influential bloggers at the periphery. The six node centrality metrics we analyzed are highly correlated, showing that an aggregate centrality score as a measure of influence will be stable to variations in centrality metrics.

Aegis: A semantic implementation of privacy as contextual integrity in social ecosystems

Combining and incorporating rich semantics of user social data, which is currently fragmented and managed by proprietary applications, has the potential to more accurately represent a users social ecosystems. However, social ecosystems raise even more serious privacy concerns than todays social networks. This paper proposes to model privacy as contextual integrity by using semantic web tools and focuses on defining default privacy policies, as they have the highest impact. Through a real implementation and performance evaluation we show that such a framework is practical.

Cultures in Community Question Answering

CQA services are collaborative platforms where users ask and answer questions. We investigate the influence of national culture on peoples online questioning and answering behavior. For this, we analyzed a sample of 200 thousand users in Yahoo Answers from 67 countries. We measure empirically a set of cultural metrics defined in Geert Hofstedes cultural dimensions and Robert Levines Pace of Life and show that behavioral cultural differences exist in community question answering platforms. We find that national cultures differ in Yahoo Answers along a number of dimensions such as temporal predictability of activities, contribution-related behavioral patterns, privacy concerns, and power inequality.

Out of the wild: On generating default policies in social ecosystems

Combining and incorporating rich semantics of user social data, which is currently fragmented and managed by proprietary applications, has the potential to more accurately represent a users social ecosystems. However, social ecosystems raise even more serious privacy concerns than todays social networks. This paper proposes to model privacy as contextual integrity by using semantic web tools and focuses on defining default privacy policies, as they have the highest impact.

The good, the bad and the deviant in community question answering

Abstract Community question answering (CQA) are collaborative online places where members ask questions for others to answer. Community members on these platforms share their expertise on various topics, from mechanical repairs to parenting. As a crowd-sourced service, such platforms not only depend on user-provided questions and answers, but also rely on their users for monitoring and flagging content that violates community rules. This study focuses on user-reported flags to characterize the behavior of the good guys and bad guys in a popular community question answering, Yahoo Answers. Conventional wisdom is to eliminate the users who receive many flags. However, our analysis of a year of traces from Yahoo Answers shows that the number of flags does not tell the full story: on one hand, users with many flags may still contribute positively to the community. On the other hand, users who never get flagged are found to violate community rules and get their accounts suspended. This analysis, however, also shows that abusive users are betrayed by their network properties: we find strong evidence of homophilous behavior and use this finding to detect abusive users who go under the community radar. Based on our empirical observations, we build a classifier that is able to detect abusive users with an accuracy as high as 83%.

The network of faults: a complex network approach to prioritize test cases for regression testing

Regression testing is performed to provide confidence that changes in a part of software do not affect other parts of the software. An execution of all existing test cases is the best way to re-establish this confidence. However, regression testing is an expensive process—there might be insufficient resources (e.g., time, workforce) to allow for the re-execution of all test cases. Regression test prioritization techniques attempt to re-order a regression test suite based on some criteria so that highest priority test cases are executed earlier. In this study, we prioritize test cases for regression testing based on the dependency network of faults. In software testing, it is common that some faults are the consequences of other faults (leading faults). Dependent faults can be removed if and only if the leading faults have been removed. Our goal is to prioritize test cases so that test cases that have exposed the leading faults in the system testing phase, are executed first in regression testing. The leading faults are modeled as the most central faults in the fault dependency network. We present ComReg, a test-case prioritization technique based on the dependency network of faults. We model a fault dependency network as a directed graph and identify leading faults to prioritize test cases for regression testing. We use a centrality aggregation technique which considers six network representative centrality metrics to identify leading faults in the fault dependency network. We also discuss the use of fault communities to select an arbitrary percentage of the test cases from a prioritized regression test suite. We conduct a case study that evaluates the effectiveness and applicability of the proposed method. We obtain a fault dependency network from the development of a vocabulary learning software. We found that the fault network is a small-world graph with distinguishable community structure. The leading faults are common in all centralities and a re-ordering of test cases is feasible for regression testing based on those leading faults. Our method outperforms traditional regression testing prioritization techniques in detecting fault dependencies. Our modeling of the network of faults provides insights into the requirement of recognizing fault dependencies while re-ordering regression test suites for both research and practice. The dependency model needs further evaluation and improvement considering relevant resources (e.g., man-hours).