Social media provide access to behavioural data at an unprecedented scale and granularity. However, using these data to understand phenomena in a broader population is difficult due to their nonrepresentativeness and the bias of statistical inference tools towards dominant languages and groups. While demographic attribute inference could be used to mitigate such bias, current techniques are almost entirely monolingual and fail to work in a global environment. We address these challenges by combining multilingual demographic inference with post-stratification to create a more representative population sample. To learn demographic attributes, we create a new multimodal deep neural architecture for joint classification of age, gender, and organization-status of social media users that operates in 32 languages. This method substantially outperforms current state of the art while also reducing algorithmic bias. To correct for sampling biases, we propose fully interpretable multilevel regression methods that estimate inclusion probabilities from inferred joint population counts and ground-truth population counts. In a large experiment over multilingual heterogeneous European regions, we show that our demographic inference and bias correction together allow for more accurate estimates of populations and make a significant step towards representative social sensing in downstream applications with multilingual social media.

People’s activities and opinions recorded as digital traces online, especially on social media and other web-based platforms, offer increasingly informative pictures of the public. They promise to allow inferences about populations beyond the users of the platforms on which the traces are recorded, representing real potential for the social sciences and a complement to survey-based research. But the use of digital traces brings its own complexities and new error sources to the research enterprise. Recently, researchers have begun to discuss the errors that can occur when digital traces are used to learn about humans and social phenomena. This article synthesizes this discussion and proposes a systematic way to categorize potential errors, inspired by the Total Survey Error (TSE) framework developed for survey methodology. We introduce a conceptual framework to diagnose, understand, and document errors that may occur in studies based on such digital traces. While there are clear parallels to the well-known error sources in the TSE framework, the new “Total Error Framework for Digital Traces of Human Behavior on Online Platforms” (TED-On) identifies several types of error that are specific to the use of digital traces. By providing a standard vocabulary to describe these errors, the proposed framework is intended to advance communication and research about using digital traces in scientific social research.

This qualitative and interpretative work aims at approaching Wikipedia's cultural facet from a transdisciplinary view. For this purpose, we set to analyse the existing literature that has dealt with Wikipedia in the generic media and in academic publications on the fields of Artificial Intelligence, Information and Education Sciences and Translation Studies as well. In these works-mostly with a descriptive, empirical or pragmatic approach-Wikipedia is referred to as an online collaborative encyclopaedia that incorporates innovation and content creation devices from the free software movement. Our proposal calls for resorting to theoretical works envisaging Wikipedia's cultural dimension. With a semiotic approach, we have based our analysis hypothesis on the social imaginary proposed by Vázquez Medel, as well as on the Polysystem Theory written by Even-Zohar. We will outline that these authors provide many clues for approaching Wikipedia as a cultural system boasting as a repository of knowledge and human relationships.

In this paper we examine the use of crowdsourcing as a means to detect Linked Data quality problems that are difficult to uncover automatically. We base our approach on the analysis of the most common errors encountered in the DBpedia dataset, and a classification of these errors according to the extent to which they are likely to be amenable to crowdsourcing. We then propose and study different crowdsourcing approaches to identify these Linked Data quality issues, employing DBpedia as our use case: (i) a contest targeting the Linked Data expert community, and (ii) paid microtasks published on Amazon Mechanical Turk. We secondly focus on adapting the Find-Fix-Verify crowdsourcing pattern to exploit the strengths of experts and lay workers. By testing two distinct Find-Verify workflows (lay users only and experts verified by lay users) we reveal how to best combine different crowds' complementary aptitudes in Linked Data quality issue detection. Empirical results show that a combination of the two styles of crowdsourcing is likely to achieve more effective results than each of them used in isolation, and that human computation is a promising and affordable way to enhance the quality of DBpedia.

In the past few years, Reddit -a community-driven platform for submitting, commenting and rating links and text posts -has grown exponentially, from a small community of users into one of the largest online communities on the Web. To the best of our knowledge, this work represents the most comprehensive longitudinal study of Reddit's evolution to date, studying both (i) how user submissions have evolved over time and (ii) how the community's allocation of attention and its perception of submissions have changed over 5 years based on an analysis of almost 60 million submissions. Our work reveals an ever-increasing diversification of topics accompanied by a simultaneous concentration towards a few selected domains both in terms of posted submissions as well as perception and attention. By and large, our investigations suggest that Reddit has transformed itself from a dedicated gateway to the Web to an increasingly self-referential community that focuses on and reinforces its own user-generated image-and textual content over external sources.