ACM Transactions on

Social Computing (TSC)

Latest Articles

Online Idea Management for Civic Engagement: A Study on the Benefits of Integration with Social Networking

Idea Management (IM) has increasingly been adopted in the civic domain as a tool to engage the citizenry in processes oriented toward innovating plans, policies, and services. While Idea Management Systems (IMSs), the software systems that instrument IM, definitely help manage this practice, they require citizens to be committed to a separate... (more)

Mi Casa es su Casa? Examining Airbnb Hospitality Exchange Practices in a Developing Economy

We present a study involving twenty in-depth, semi-structured interviews, a street survey, and... (more)

Deadline-Aware Fair Scheduling for Multi-Tenant Crowd-Powered Systems

Crowdsourcing has become an integral part of many systems and services that deliver high-quality results for complex tasks such as data linkage,... (more)

Activity Archetypes in Question-and-Answer (Q8A) Websites—A Study of 50 Stack Exchange Instances

Millions of users on the Internet discuss a variety of topics on Question-and-Answer (Q8A)... (more)


About TSC

ACM Transactions on Social Computing (TSC) seeks to publish work that covers the full spectrum of social computing including theoretical, empirical, systems, and design research contributions. READ MORE

Special Issue on Negotiating Truth and Trust in Socio-Technical Systems

This special issue will explore interdisciplinary perspectives on negotiating truth and trust in socio-technical systems. Rather than seeking to define or promote one "truth," this issue focuses on how users identify and wrestle with competing notions of truth and trust in highly contested online information environments, full of risk and reward, and how designs of infrastructure and policy help or hinder these interactions.  

Special Issue on Emoji Understanding and Applications in Social Media

This special issue seeks original research manuscripts dealing with computer and social science research efforts on understanding social, cultural, communicative, and linguistic roles of emoji and on building novel computational methods to understand, interpret, and exploit them. READ MORE

Forthcoming Articles
Uncertainty-based False Information Propagation in Social Networks

Many network scientists have investigated the research problem of mitigating or removing false information propagated in social networks. There are two main categories of false information. The first is disinformation, which represents false information that is knowingly shared with malicious intent. The second is misinformation, in which agents share false information unwittingly, without any malicious intent. Many existing works have looked at mechanisms to mitigate or remove false information in terms of how to select a set of seeding nodes (or agents) based on their network characteristics (e.g., centrality features). However, little work has focused on the role of uncertainty as a factor in the formulation of agents' opinions. Uncertainty-aware agents can form different opinions and eventual beliefs about true or false information. In this work, we leverage an opinion model, called Subjective Logic (SL), which explicitly deals with a level of uncertainty in an opinion where the opinion is defined as a combination of belief, disbelief, and uncertainty and the level of uncertainty is easily interpreted as a person's confidence in given belief or disbelief. However, SL considers the dimension of uncertainty only derived from a lack of information (i.e., ignorance), not from other causes such as conflicting evidence. In the era of Big Data where we are flooded with information, conflicting information can increase uncertainty (or ambiguity) and have a greater effect on opinions than a lack of information (or ignorance). In order to enhance the capability of SL to deal with ambiguity as well as ignorance, we propose an SL-based opinion model that includes a level of uncertainty derived from both causes. By developing a variant of the SIR (Susceptible-Infected-Recovered) model that can change an agent's status based on the state of their opinions, we capture the evolution of agents' opinions over time. We present an analysis and discussion of critical changes under varying values of key design parameters, including the frequency ratio of true or false information propagation, centrality metrics used for selecting seeding false informers and true informers, an opinion decay factor, the degree of agents' prior belief, and the percentage of true informers. We validated our proposed opinion model using both the synthetic network environments and realistic network environments considering a real network topology, user behaviors, and the quality of news articles. The proposed agent's opinion model and corresponding strategies to deal with false information can be applicable to combat the spread of fake news in various social media platforms (e.g., Facebook).

Beyond monetary incentives: experiments in paid microtask contests

In this paper, we aim to gain a better understanding into how paid microtask crowdsourcing could leverage its appeal and scaling power by using contests to boost crowd performance and engagement. We introduce our microtask-based annotation platform Wordsmith, which features incentives such as points, leaderboards and badges on top of financial remuneration. Our analysis focuses on a particular type of incentive, contests, as a means to apply crowdsourcing in near-real-time scenarios, in which requesters need labels quickly. We model crowdsourcing contests as a continuous-time Markov chain with the objective to maximise the output of the crowd workers, while varying a parameter which determines whether a worker is eligible for a reward based on their present rank on the leaderboard. We conduct empirical experiments in which crowd workers recruited from CrowdFlower carry out annotation microtasks on Wordsmith - in our case, to identify named entities in a stream of Twitter posts. In the experimental conditions, we test different reward spreads and record the total number of annotations received. We compare the results against a control condition in which the same annotation task was completed on CrowdFlower without a time or contest constraint. The experiments show that rewarding only the best contributors in a live contest could be a viable model to deliver results faster, though quality might suffer for particular types of annotation tasks. Increasing the reward spread leads to more work being completed, especially by the top contestants. Overall, the experiments shed light on possible design improvements of paid microtasks platforms to boost task performance and speed, and make the overall experience more fair and interesting for crowd workers.

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