A black market for upvotes and likes

Purpose: This research investigates controversial online marketing techniques that involve buying hundreds or even thousands of upvotes, likes, comments, etc. Methodology: Observation and categorization of 7,426 campaigns posted on the crowdsourcing platform microworkers.com over a 365 day (i.e., yearlong) period were conducted. Hypotheses about the mechanics and effectiveness of these campaigns were established and evaluated. Findings: The campaigns contained a combined 1,856,316 microtasks, 89.7% of which were connected to online promotion. Techniques for search engine manipulation, comment-generating in the scale of tens of thousands, online vote manipulation, mass account creation, methods for covering tracks were discovered. The article presents an assessment of the effectiveness of such campaigns as well as various security challenges created by these campaigns. Research limitations: The observed campaigns form only a small portion of the overall "black market". This is due to invite-only campaigns and the presence of alternative, unobservable platforms. Practical implications: The findings of this article could be input for detecting and avoiding such online campaigns. Social implications: The findings show that in some conditions tremendous levels of manipulation of an online discourse can be achieved with a limited budget. Originality: While there is related work on "follower factories" and "click/troll farms", those entities offer complete "solutions" and their techniques are rather opaque. By investigating a crowdsourcing platform, this research unveils the underlying mechanics and organization of such campaigns. The research is based on a uniquely large number of observations. Small, cheap campaigns, the manipulation of less significant platforms is also included, while the related work tends to focus on mass, politically motivated efforts.Comment: 20 pages, 1 figur

Crowdsourced network measurements: Benefits and best practices

Network measurements are of high importance both for the operation of networks and for the design and evaluation of new management mechanisms. Therefore, several approaches exist for running network measurements, ranging from analyzing live traffic traces from campus or Internet Service Provider (ISP) networks to performing active measurements on distributed testbeds, e.g., PlanetLab, or involving volunteers. However, each method falls short, offering only a partial view of the network. For instance, the scope of passive traffic traces is limited to an ISP’s network and customers’ habits, whereas active measurements might be biased by the population or node location involved. To complement these techniques, we propose to use (commercial) crowdsourcing platforms for network measurements. They permit a controllable, diverse and realistic view of the Internet and provide better control than do measurements with voluntary participants. In this study, we compare crowdsourcing with traditional measurement techniques, describe possible pitfalls and limitations, and present best practices to overcome these issues. The contribution of this paper is a guideline for researchers to understand when and how to exploit crowdsourcing for network measurements

Combating Crowdsourced Manipulation of Social Media

Crowdsourcing systems - like Ushahidi (for crisis mapping), Foldit (for protein folding) and Duolingo (for foreign language learning and translation) - have shown the effectiveness of intelligently organizing large numbers of people to solve traditionally vexing problems. Unfortunately, new crowdsourcing platforms are emerging to support the coordinated dissemination of spam, misinformation, and propaganda. These “crowdturfing” systems are a sinister counterpart to the enormous positive opportunities of crowdsourcing; they combine the organizational capabilities of crowdsourcing with the ability to widely spread artificial grass root support (so called “astroturfing”). This thesis begins a study of crowdturfing that targets social media and proposes a framework for “pulling back the curtain” on crowdturfers to reveal their underlying ecosystem. Concretely, this thesis (i) analyzes the types of campaigns hosted on multiple crowdsourcing sites; (ii) links campaigns and their workers on crowdsourcing sites to social media; (iii) analyzes the relationship structure connecting these workers, their profile, activity, and linguistic characteristics, in comparison with a random sample of regular social media users; and (iv) proposes and develops statistical user models to automatically identify crowdturfers in social media. Since many crowdturfing campaigns are hidden, it is important to understand the potential of learning models from known campaigns to detect these unknown campaigns. Our experimental results show that the statistical user models built can predict crowdturfers with very high accuracy

EVOLUTION OF CROWD-SOURCING FROM COMPUTER TO MOBILE SYSTEMS

As crowd-sourcing is becoming popular for problem solving and completing a task, it is now very important to use this concept in an advance manner. It can also be used as a distributed and vast source of information. This concept is now evolving in world of mobile systems. This will be a little different from that of computer systems. In this paper, we have discussed some new technologies and challenges before us to implement these advancements in crowd-sourcing. We are going to talk about cheat-detection techniques, handling multimedia databases and how to trade off between cost and accuracy by considering the redundant data as well

Unemployment and online labor

Online labor markets experienced a rapid growth in recent years. They allow for long-distance transactions and offer workers access to a potentially ‘global’ pool of labor demand. As such, they bear the potential to act as a substitute for shrinking local income opportunities. Using detailed U.S. data from a large online labor platform for microtasks, we study how local unemployment affects participation and work intensity online. We find that, at the extensive margin, an increase in commuting zone level unemployment is associated with more individuals joining the platform and becoming active in fulfilling tasks. At the intensive margin, our results show that with higher unemployment rates, online labor supply becomes more elastic. These results are driven by a decrease of the reservation wage during standard working hours. Finally, the effects are transient and do not translate to a permanent increase in platform participation by incumbent users. Our findings highlight that many workers consider online labor markets as a substitute to offline work for generating income, especially in periods of low local labor demand. However, the evidence also suggests that, despite their potential to attract workers, online markets for microtasks are currently not viable as a long run alternative for most workers

Components and Functions of Crowdsourcing Systems – A Systematic Literature Review

Many organizations are now starting to introduce crowdsourcing as a new model of business to outsource tasks, which are traditionally performed by a small group of people, to an undefined large workforce. While the utilization of crowdsourcing offers a lot of advantages, the development of the required system carries some risks, which are reduced by establishing a profound theoretical foundation. Thus, this article strives to gain a better understanding of what crowdsourcing systems are and what typical design aspects are considered in the development of such systems. In this paper, the author conducted a systematic literature review in the domain of crowdsourcing systems. As a result, 17 definitions of crowdsourcing systems were found and categorized into four perspectives: the organizational, the technical, the functional, and the human-centric. In the second part of the results, the author derived and presented components and functions that are implemented in a crowdsourcing system