A Field Trial on Mobile Crowdsourcing of News Content Factors Influencing Participation

Abstract

Abstract. We conducted a five-week field trial on mobile crowdsourcing of hyperlocal news content to 1) understand the readers' experiences and 2) explore factors affecting their participation. In the end of the study the participants were surveyed with an online questionnaire (17/104 respondents) and five participants were interviewed. Although respondents and interviewees were enthusiastic about the trial, the activity in the trial was low. Results indicate that participant characteristics (age, gender, participation motivations and hobbyist background in photography) and task characteristics in terms of the subjectively perceived task significance (possible impact on important issues in the environment or on community), task relevance (related to the background and participation motivation), and task engagingness have an effect on the participation. In addition, participation was influenced by the estimated needed effort vs. the expected benefit (monetary benefit or having a possibility to influence), vicinity to the assignment location, enjoyment of the activity, and the monetary reward. To plan and manage the crowdsourcing activity the news publishers need information about the characteristics of the participants, participation patterns and motivations that could be provided by the crowdsourcing platform.

[Retracted] Construction and Case Analysis of Sensor News Whole Chain Production Model Based on Artificial Intelligence Technology

“…One is to obtain the required data from the existing sensing resources in government organizations and public facilities [16]. Second, collect data through mobile terminals and intelligent devices with built-in sensor settings [17]. The third is to obtain data information through public participation in sensing experience.…”

Mobile Crowdsourcing in Smart Cities: Technologies, Applications, and Future Challenges

“…The bidding method effectively coordinates the profits of the platform and participants to promote their cooperation. f) Workers: the platform generally describes a worker from several aspects, such as working time, location, age, gender, work, preference, and device type [58,65,77]. The basic factors a worker considers in performing MCS tasks are time, position, and preference, which are explained as follows:…”

“…• Preferences: a MCS should consider the interests and preferences of workers in tasks allocation. For example, the interest of workers in collecting photos are very different [57,65]. Thus, the platform should recommend tasks to workers based on their interests.…”

“…The auction model is strategy-proof, individual rational, and ensure budget balance Wang emphet al [64] Presenting an auction model for quality-aware and fine-grained MCS The mechanism achieves truthfulness, individual rationality and computational efficiency Vaataja et al [65] Discussing the experiences and factors that affect the participation preferences Using mobile assignments for crowdsourcing in a real-world field trial Mashhadi et al [66] Technique that employes users' mobility pattern and contribution history to estimate their credibility Estimating the quality of contributions based on the contributor's mobility, as well as their trustworthiness score Huang et al [67] A reputation system for evaluating the trustworthiness of volunteer contributions in participatory sensing…”

An architecture for adhesive mobile crowdsourcing application

“…A task is the assignment being offered to be solved by the crowd and a solver is participating crowd who attempt to do the assignment. Mobile crowdsourcing is an instance of crowdsourcing where the task is made available and accessed through mobile devices, so do the solutions [2]. Solvers are remotely connected to each other and they do not know each other.…”