Adaptive and Interoperable Crowdsourcing

Crowd-based computing is an increasingly popular paradigm for building Web applications, which uses the collective strength of human actors for performing tasks that are more suited to humans than computers. Interaction with the crowds was originally confined to specifically designed crowdsourcing platforms, such as Amazon Mechanical Turk. More recently, crowd-based computing has been reconsidered and extended, targeting social networks such as Facebook, Twitter, or LinkedIn, or including basic and direct interaction mechanisms, such as routing personal emails or tweets. Crowdsearcher, the system presented here, fosters interoperability and adaptation in crowd-based applications -- for example, the ability of supporting multiplatform applications and adapting them in reaction to events. This approach specifically supports dynamic interoperability (that is, the ability to modify the execution platforms while the application is ongoing) as a reaction to crowd behavior, which is hardly predictable. The authors show how to specify interoperability control at a high, declarative level and then implement it using active rules, thereby obtaining answers from crowds engaged in different communities. They also show the approach's effect on precision, delay, and cost

Designing Complex Crowdsourcing Applications Covering Multiple Platforms and Tasks

A number of emerging crowd-based applications cover very different scenarios, including opinion mining, multimedia data annotation, localised information gathering, marketing campaigns, expert response gathering, and so on. In most of these scenarios, applications can be decomposed into tasks that collectively produce their results; tasks interactions give rise to arbitrarily complex workflows. In this paper we propose methods and tools for designing crowd-based workflows as interacting tasks. We describe the modelling concepts that are useful in this framework, including typical workflow patterns, whose function is to decompose a cognitively complex task into simple interacting tasks for cooperative solving.}{We then discuss how workflows and patterns are managed by CrowdSearcher, a system for designing, deploying and monitoring applications on top of crowd-based systems, including social networks and crowdsourcing platforms. Tasks performed by humans consist of simple operations which apply to homogeneous objects; the complexity of aggregating and interpreting task results is embodied within the framework. We show our approach at work on a validation scenario and we report quantitative findings, which highlight the effect of workflow design on the final results

Studying Multicultural Diversity of Cities and Neighborhoods through Social Media Language Detection

Cities are growing as melting pots of people with different culture, religion, and language. In this paper, through multilingual analysis of Twitter contents shared within a city, we analyze the prevalent language in the different neighborhoods of the city and we compare the results with census data, in order to highlight any parallelisms or discrepancies between the two data sources. We show that the officially identified neighborhoods are actually representing significantly different communities and that the use of the social media as a data source helps to detect those weak signals that are not captured from traditional data

Studying Multicultural Diversity of Cities and Neighborhoods through Social Media Language Detection

reserved7Arnaboldi, M.; Brambilla, M.; Cassottana, B.; Ciuccarelli, P.; Ripamonti, D.; Vantini, S.; Volonterio, R.Arnaboldi, Michela; Brambilla, Marco; Cassottana, Beatrice; Ciuccarelli, Paolo; Ripamonti, DAVIDE MARIA FILIPPO; Vantini, Simone; Volonterio, Riccard