Fabian Flöck

Zijian Wang, Scott A. Hale, David Adelani et al.

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 non-representativeness 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.

Lisa Posch, Arnim Bleier, Fabian Flöck et al.

Crowd employment is a new form of short term employment that has been rapidly becoming a source of income for a vast number of people around the globe. It differs considerably from more traditional forms of work, yet similar ethical and optimization issues arise. One key to tackle such challenges is to understand what motivates the international crowd workforce. In this work, we study the motivation of workers involved in one particularly prevalent type of crowd employment: micro-tasks. We report on the results of applying the Multidimensional Crowdworker Motivation Scale (MCMS) in ten countries, which unveil significant international differences.

Fabian Flöck, Kenan Erdogan, Maribel Acosta

We present a dataset that contains every instance of all tokens (~ words) ever written in undeleted, non-redirect English Wikipedia articles until October 2016, in total 13,545,349,787 instances. Each token is annotated with (i) the article revision it was originally created in, and (ii) lists with all the revisions in which the token was ever deleted and (potentially) re-added and re-deleted from its article, enabling a complete and straightforward tracking of its history. This data would be exceedingly hard to create by an average potential user as it is (i) very expensive to compute and as (ii) accurately tracking the history of each token in revisioned documents is a non-trivial task. Adapting a state-of-the-art algorithm, we have produced a dataset that allows for a range of analyses and metrics, already popular in research and going beyond, to be generated on complete-Wikipedia scale; ensuring quality and allowing researchers to forego expensive text-comparison computation, which so far has hindered scalable usage. We show how this data enables, on token-level, computation of provenance, measuring survival of content over time, very detailed conflict metrics, and fine-grained interactions of editors like partial reverts, re-additions and other metrics, in the process gaining several novel insights.

Lisa Posch, Arnim Bleier, Clemens Lechner et al.

Crowd employment is a new form of short-term and flexible employment which has emerged during the past decade. In order to understand this new form of employment, it is crucial to illuminate the underlying motivations of the workforce involved in it. This paper introduces the Multidimensional Crowdworker Motivation Scale (MCMS), a scale for measuring the motivation of crowdworkers on micro-task platforms. The MCMS is theoretically grounded in self-determination theory and tailored specifically to the context of paid crowdsourced micro-labor. The scale measures the motivation of crowdworkers along six motivational dimensions, ranging from amotivation to intrinsic motivation. We validated the MCMS on data collected in ten countries and three income groups. Factor analyses demonstrated that the MCMS's six dimensions showed good model fit, validity, and reliability. Furthermore, our measurement invariance tests showed that motivations measured with the MCMS are comparable across countries and income groups, and we present a first cross-country comparison of crowdworker motivations. This work constitutes an important first step towards understanding the motivations of the international crowd workforce.

Martin Körner, Tatiana Sennikova, Florian Windhäuser et al.

Wikipedia articles about the same topic in different language editions are built around different sources of information. For example, one can find very different news articles linked as references in the English Wikipedia article titled "Annexation of Crimea by the Russian Federation" than in its German counterpart (determined via Wikipedia's language links). Some of this difference can of course be attributed to the different language proficiencies of readers and editors in separate language editions, yet, although including English-language news sources seems to be no issue in the German edition, English references that are listed do not overlap highly with the ones in the article's English version. Such patterns could be an indicator of bias towards certain national contexts when referencing facts and statements in Wikipedia. However, determining for each reference which national context it can be traced back to, and comparing the link distributions to each other is infeasible for casual readers or scientists with non-technical backgrounds. Wikiwhere answers the question where Web references stem from by analyzing and visualizing the geographic location of external reference links that are included in a given Wikipedia article. Instead of relying solely on the IP location of a given URL our machine learning models consider several features.