Most users on social media have intrinsic characteristics, such as interests and political views, that can be exploited to identify and track them, thus raising privacy and identity concerns in online communities. In this paper we investigate the problem of user identity linkage on two behavior datasets collected from different experiments. Specifically, we focus on user linkage based on users' interaction behaviors with respect to content topics. We propose an embedding method to model a topic as a vector in a latent space so as to interpret its deep semantics. Then a user is modeled as a vector based on his or her interactions with topics. The embedding representations of topics are learned by optimizing the joint-objective: the compatibility between topics with similar semantics, the discriminative abilities of topics to distinguish identities, and the consistency of the same user's characteristics from two datasets. The effectiveness of our method is verified on real-life datasets and the results show that it outperforms related methods. We also analyze failure cases in the application of our identity linkage method. Our analysis shows that factors such as the visibility and variance of user behaviors and user's group psychology can result in mis-linkages. We also analyze the details of the behaviors of some representative users to understand the essential reasons for their identity being mis-linked. We find that these users have high variance level in their behaviors. According to the above experimental results, we introduce a confidence score into identity linkage to provide information about the accuracy of the method results.
Today's online question and answer (Q&A) services are receiving a large volume of questions. It becomes increasingly challenging to motivate domain experts to provide quick and high-quality answers. Recent systems seek to engage real-world experts by allowing them to set a price on their answers. This leads to a ``targeted'' Q&A model where users to ask questions to a target expert by paying the corresponding price. In this paper, we perform a case study on two emerging targeted Q&A systems Fenda (China) and Whale (US) to understand how monetary incentives affect user behavior. By analyzing a large dataset of 220K questions (worth 1 million USD), we find that payments indeed enable quick answers from experts, but also drive certain users to game the system for profits. In addition, this model requires users (experts) to proactively adjust their price to make profits. People who are unwilling to lower their prices are likely to hurt their income and engagement over time.
GROUP 2018 Special Issue Guest Editorial
Permeability, Interoperability and Velocity: Entangled Dimensions of Infrastructural Grind at the Intersection of Blockchain and Shipping
Online Idea Management for Civic Engagement: A Study on the Benefits of Integration with Social Networking
Community of practice (CoP) is a primary framework in social computing research that addresses learning and organizing specific practices in online communities. However, the classic CoP theory does not provide a detailed account for how practices change or evolve. Against the backdrop of a rapidly changing occupational landscape, it is crucial to understand how people participate in online communities focused on practices that have a volatile nature, as well as how social computing tools can best support them. In this paper, we examine user experience (UX) design as a volatile practice that has no coherent body of knowledge and lacks a concrete path for newcomers to become a UX professional. Our study site is the ?/r/userexperience? subreddit, an online UX community where practitioners socialize and learn. Using a mixed-methods approach, we identified five distinct social roles in relation to knowledge production and dissemination in the online community of volatile practice. We demonstrate that knowledge production is highly distributed, involving the participation and sensemaking of community members of varied levels of experience. We discuss how online platforms support online community of volatile practice.
FOSS communities are increasingly employing a sponsored participation model where free, open source software development is combined with paid customer support and feature development to ensure community sustainability. This makes it difficult for peripheral users, who are not part of the core administrative or sponsoring organization to participate meaningfully. The research study presented in this paper explores how a hybrid FOSS community ? one where commercial and volunteer developer effort are combined to deliver a free/libre, open source software product ? enables or constrains the participation of product users. Learning how to act in a system of situated cognition requires a ?cognitive apprenticeship? that teaches participants how to engage in authentic domain activity ? i.e. how to engage in community processes, Learning how to participate in a distributed system of social cognition requires a ?social apprenticeship? by which participants become enculturated in the system of meanings, values, norms, and behaviors that govern community identity and membership and learn to build social capital. These forms of apprenticeship were related to the cognitive and social translations produced by affordances of the socio-technical participation architecture, to demonstrate how affordances support two interrelated forms of understanding, which act to support participation in very different ways. The contribution to theory is to provide an interior view of key community participation mechanisms, affordances, and cognitive (knowledge-related) or social (identity and worldview related) translations revealed as non-technical users engage with Hybrid FOSS community processes. This perspective has largely been missing from the literature. Our contribution to practice is provided by the explanation of how four distinct categories of affordance provide cognitive and social apprenticeship benefits. These provide a framework which, when coupled with the detailed examples of affordances provided in the paper, will allow designers of online community participation architecture to plan for legitimate peripheral user participation.
Cyberbullying is a major cyber issue that is common among adolescents. Recent reports show that more than one out of five students in the United States is a victim of cyberbullying. Majority of cyberbullying incidents occur on public social media platforms such as Twitter. Automated cyberbullying detection methods can help prevent cyberbullying before the harm is done on the victim. In this study, we analyze two corpora of cyberbullying Tweets from similar incidents to construct and validate an automated detection model. Our method emphasizes on the two claims that are supported by our results. First, despite other approaches that assume that cyberbullying instances use vulgar or profane words, we show that they do not necessarily contain negative words. Second, we highlight the importance of context and the characteristics of actors involved and their position in the network structure in detecting cyberbullying rather than only considering the textual content in our analysis.
Targeted social media advertising based on psychometric user profiling has emerged as an effective way of reaching individuals who are predisposed to accept and be persuaded by the advertising message. This paper argues that in the case of political advertising, this may present a democratic and ethical challenge. Hypertargeting methods such as psychometrics can ?crowd out? political communication with opposing views due to individual attention and time limitations, creating inequities in the access to information essential for voting decisions. The use of psychometrics also appears to have been used to spread both information and misinformation through social media in recent elections in the U.S. and Europe. This paper is an applied ethics study of these methods in the context of democratic processes and compared to purely commercial situations. The ethical approach is based on the theoretical, contractarian work of John Rawls which serves as a lens through which the author examines whether the rights of individuals, as Rawls attributes them, are violated by this practice. The paper concludes that within a Rawlsian framework, use of psychometrics in commercial advertising on social media platforms, though not immune to criticism, is not necessarily unethical. In a democracy, however, the individual cannot abandon the consumption of political information, and since using psychometrics in political campaigning makes access to such information unequal, it violates Rawlsian ethics and should be regulated.