Crowdsourcing

About: Crowdsourcing is a research topic. Over the lifetime, 12889 publications have been published within this topic receiving 230638 citations.

CrisisTracker: crowdsourced social media curation for disaster awareness

Abstract: Victims, volunteers, and relief organizations are increasingly using social media to report and act on large-scale events, as witnessed in the extensive coverage of the 2010-2012 Arab Spring uprisings and 2011 Japanese tsunami and nuclear disasters. Twitter® feeds consist of short messages, often in a nonstandard local language, requiring novel techniques to extract relevant situation awareness data. Existing approaches to mining social media are aimed at searching for specific information, or identifying aggregate trends, rather than providing narratives. We present CrisisTracker, an online system that in real time efficiently captures distributed situation awareness reports based on social media activity during large-scale events, such as natural disasters. CrisisTracker automatically tracks sets of keywords on Twitter and constructs stories by clustering related tweets on the basis of their lexical similarity. It integrates crowdsourcing techniques, enabling users to verify and analyze stories. We report our experiences from an 8-day CrisisTracker pilot deployment during 2012 focused on the Syrian civil war, which processed, on average, 446,000 tweets daily and reduced them to consumable stories through analytics and crowdsourcing. We discuss the effectiveness of CrisisTracker based on the usage and feedback from 48 domain experts and volunteer curators.

Crowdsourcing based business models: In search of evidence for innovation 2.0

Abstract: Open innovation has gained increased attention as a potential paradigm for improving innovation performance. This paper addresses crowdsourcing, an under-researched type of open innovation that is often enabled by the web. We focus on a type of crowdsourcing where financial rewards exist, where a crowd is tasked with solving problems which solution seekers anticipate to be empirically provable, but where the source of solutions is uncertain and addressing the challenge in-house perceived to be too high-risk. There is a growing recourse to crowdsourcing, but we really know little about its effectiveness, best practices, challenges and implications. We consider the shift to more open innovation trajectories over time, define crowdsourcing as an open innovation model, and clarify how crowdsourcing differs from other types of 'open' innovation (e.g. outsourcing and open-source). We explore who is crowdsourcing and how, looking at the potential diversity and core features and variables implicated in crowdsourcing models.

Piggyback CrowdSensing (PCS): energy efficient crowdsourcing of mobile sensor data by exploiting smartphone app opportunities

Abstract: Fueled by the widespread adoption of sensor-enabled smartphones, mobile crowdsourcing is an area of rapid innovation. Many crowd-powered sensor systems are now part of our daily life -- for example, providing highway congestion information. However, participation in these systems can easily expose users to a significant drain on already limited mobile battery resources. For instance, the energy burden of sampling certain sensors (such as WiFi or GPS) can quickly accumulate to levels users are unwilling to bear. Crowd system designers must minimize the negative energy side-effects of participation if they are to acquire and maintain large-scale user populations.To address this challenge, we propose Piggyback CrowdSensing (PCS), a system for collecting mobile sensor data from smartphones that lowers the energy overhead of user participation. Our approach is to collect sensor data by exploiting Smartphone App Opportunities -- that is, those times when smartphone users place phone calls or use applications. In these situations, the energy needed to sense is lowered because the phone need no longer be woken from an idle sleep state just to collect data. Similar savings are also possible when the phone either performs local sensor computation or uploads the data to the cloud. To efficiently use these sporadic opportunities, PCS builds a lightweight, user-specific prediction model of smartphone app usage. PCS uses this model to drive a decision engine that lets the smartphone locally decide which app opportunities to exploit based on expected energy/quality trade-offs.We evaluate PCS by analyzing a large-scale dataset (containing 1,320 smartphone users) and building an end-to-end crowdsourcing application that constructs an indoor WiFi localization database. Our findings show that PCS can effectively collect large-scale mobile sensor datasets (e.g., accelerometer, GPS, audio, image) from users while using less energy (up to 90% depending on the scenario) compared to a representative collection of existing approaches.

Motivations for Participation in a Crowdsourcing Application to Improve Public Engagement in Transit Planning

Abstract: Governments increasingly turn to the Internet to aid in transparency, accountability, and public participation activities, and there is growing interest in innovative online problem-solving models to serve the public good. One such model, the crowdsourcing model, leverages the collective intelligence of online communities for specific purposes. Understanding how and why people participate in these kinds of activities is important for developing better new media tools for the public good going forward. In 2009, the Federal Transit Administration supported the Next Stop Design project, an attempt to use crowdsourcing for public participation in transit planning. Based on interviews with 23 Next Stop Design participants, the present applied communication study discusses the motivations of those participants to engage the project.