In the entertainment domain users tweet about their expectations and opinions regarding upcoming, current and past experiences, while companies advertise and promote the shows. This... Show More

In the entertainment domain users tweet about their expectations and opinions regarding upcoming, current and past experiences, while companies advertise and promote the shows. This characterization, important for customers and companies, goes beyond traditional sentiment analysis where the polarity of the sentiments expressed in opinions is usually identified as positive, negative or neutral. The authors investigate different tweet representation models, including bags of words and probabilistic topic models, to shed light on the semantics of the messages. Their experiments show that topic-based models generated with Latent Dirichlet Allocation (LDA) yield, most of the times, better categorizations when compared to TF-IDF based features, particularly when these models are enriched with natural language features and specific Twitter slang.

Novel social media collaboration platforms, such as games with a purpose and mechanised labour marketplaces, are increasingly used for enlisting large populations of non-experts in crowdsourced... Show More

Novel social media collaboration platforms, such as games with a purpose and mechanised labour marketplaces, are increasingly used for enlisting large populations of non-experts in crowdsourced knowledge acquisition processes. Climate Quiz uses this paradigm for acquiring environmental domain knowledge from non-experts. The game’s usage statistics and the quality of the produced data show that Climate Quiz has managed to attract a large number of players but noisy input data and task complexity led to low player engagement and suboptimal task throughput and data quality. To address these limitations, the authors propose embedding the game into a hybrid-genre workflow, which supplements the game with a set of tasks outsourced to micro-workers, thus leveraging the complementary nature of games with a purpose and mechanised labour platforms. Experimental evaluations suggest that such workflows are feasible and have positive effects on the game’s enjoyment level and the quality of its output.

There is currently a trend in media management and the semantic web to develop new media processing methods and knowledge representation techniques to organize and structure media collections around... Show More

There is currently a trend in media management and the semantic web to develop new media processing methods and knowledge representation techniques to organize and structure media collections around events. While this increased interest for events as the central aggregator of media is supported by strong research in the fields of knowledge representation and computer vision; it is not yet clear how the digital era users use events when sharing their personal media collections. In this article, the authors first analyze and discuss a survey on photo-taking behavior and then explore a dataset of publicly available online albums to find out how users share photos. Based on the results of these studies, the authors show that, while media sharing services do not support events as yet, users still share their media around personal events, either by providing explicit spatio-temporal metadata in free text form, or by using an event-centric vocabulary when titling their collections of photos.

In this paper, the authors propose a novel bias detection method based on social preference learning for targets on competing topics such as “GalaxyTab vs. iPad” in Twitter. People tend to evaluate... Show More

In this paper, the authors propose a novel bias detection method based on social preference learning for targets on competing topics such as “GalaxyTab vs. iPad” in Twitter. People tend to evaluate a topic by expressing their opinions towards the associated targets such as price and quality. To exploit characteristics of social data, targets are extracted by a modified HITS algorithm on a tripartite graph. The main contribution is that social preferences are learned with explicit sentiment, latent sentiment as social semantics, and lexical sentiment as contextual semantics on targets of the topic, and that the individual preference is considered together with social preferences for the bias detection of a tweet. Experimental results on Twitter collection show significant improvements over all baseline methods. The results indicate that the method deals with not only the lack of a sentiment lexicon but also social and contextual semantics on targets of social users.

Databases of places have become increasingly popular to identify places of a given type that are close to a user-specified location. As it is important for these systems to use an up-to-date... Show More

Databases of places have become increasingly popular to identify places of a given type that are close to a user-specified location. As it is important for these systems to use an up-to-date database with a broad coverage, there is a need for techniques that are capable of expanding place databases in an automated way. In this paper the authors discuss how geographically annotated information obtained from social media can be used to discover new places. In particular, the authors first determine potential places of interest by clustering the locations where Flickr photos have been taken. The tags from the Flickr photos and the terms of the Twitter messages posted in the vicinity of the obtained candidate places of interest are then used to rank them based on the likelihood that they belong to a given type. For several place types, their methodology finds places that are not yet contained in the databases used by Foursquare, Google, LinkedGeoData and Geonames. Furthermore, the authors’ experimental results show that the proposed method can successfully identify errors in existing place databases such as Foursquare.

In this paper the authors propose a crowd coordination mechanism to increase the quality of articles produced in wiki systems. Wikis constitute promising social digital ecosystems for collaborative... Show More

In this paper the authors propose a crowd coordination mechanism to increase the quality of articles produced in wiki systems. Wikis constitute promising social digital ecosystems for collaborative knowledge creation on the Web. However, as a result of the purely self-coordinated manner that they function, they cannot ensure the quality of the produced articles - an issue that affects their reliability and acceptance. The authors show that wiki article quality optimization can be formulated as a resource allocation problem. Contributors are selected from the wiki community crowd according to their skills, and matched to the articles they can improve the most. A model of the English Wikipedia is given, parameterized and validated from recent field studies results. Experimental results were obtained with simulation systems implementing this model and on a series of scenarios, which include an analysis of the impact of using semantic relations between wiki domains. The obtained results indicate that the proposed mechanism can lead to the production of wiki articles of higher quality, compared to the respective results achieved by the fully self-coordinated wiki.