6 research outputs found
Studying Moral-based Differences in the Framing of Political Tweets
In this paper, we study the moral framing of political content on Twitter.
Specifically, we examine differences in moral framing in two datasets: (i)
tweets from US-based politicians annotated with political affiliation and (ii)
COVID-19 related tweets in German from followers of the leaders of the five
major Austrian political parties. Our research is based on recent work that
introduces an unsupervised approach to extract framing bias and intensity in
news using a dictionary of moral virtues and vices. In this paper, we use a
more extensive dictionary and adapt it to German-language tweets. Overall, in
both datasets, we observe a moral framing that is congruent with the public
perception of the political parties. In the US dataset, democrats have a
tendency to frame tweets in terms of care, while loyalty is a characteristic
frame for republicans. In the Austrian dataset, we find that the followers of
the governing conservative party emphasize care, which is a key message and
moral frame in the party's COVID-19 campaign slogan. Our work complements
existing studies on moral framing in social media. Also, our empirical findings
provide novel insights into moral-based framing on COVID-19 in Austria.Comment: Accepted for publication in ICWSM-2021 - link to published version
will be adde
The TagRec Framework as a Toolkit for the Development of Tag-Based Recommender Systems
Recommender systems have become important tools to support users in
identifying relevant content in an overloaded information space. To ease the
development of recommender systems, a number of recommender frameworks have
been proposed that serve a wide range of application domains. Our TagRec
framework is one of the few examples of an open-source framework tailored
towards developing and evaluating tag-based recommender systems. In this paper,
we present the current, updated state of TagRec, and we summarize and reflect
on four use cases that have been implemented with TagRec: (i) tag
recommendations, (ii) resource recommendations, (iii) recommendation
evaluation, and (iv) hashtag recommendations. To date, TagRec served the
development and/or evaluation process of tag-based recommender systems in two
large scale European research projects, which have been described in 17
research papers. Thus, we believe that this work is of interest for both
researchers and practitioners of tag-based recommender systems.Comment: https://github.com/learning-layers/TagRe
Reproducibility in Machine Learning-Driven Research
Research is facing a reproducibility crisis, in which the results and
findings of many studies are difficult or even impossible to reproduce. This is
also the case in machine learning (ML) and artificial intelligence (AI)
research. Often, this is the case due to unpublished data and/or source-code,
and due to sensitivity to ML training conditions. Although different solutions
to address this issue are discussed in the research community such as using ML
platforms, the level of reproducibility in ML-driven research is not increasing
substantially. Therefore, in this mini survey, we review the literature on
reproducibility in ML-driven research with three main aims: (i) reflect on the
current situation of ML reproducibility in various research fields, (ii)
identify reproducibility issues and barriers that exist in these research
fields applying ML, and (iii) identify potential drivers such as tools,
practices, and interventions that support ML reproducibility. With this, we
hope to contribute to decisions on the viability of different solutions for
supporting ML reproducibility.Comment: This research is supported by the Horizon Europe project TIER2 under
grant agreement No 10109481
Modelling the long-term fairness dynamics of data-driven targeted help on job seekers
Abstract The use of data-driven decision support by public agencies is becoming more widespread and already influences the allocation of public resources. This raises ethical concerns, as it has adversely affected minorities and historically discriminated groups. In this paper, we use an approach that combines statistics and data-driven approaches with dynamical modeling to assess long-term fairness effects of labor market interventions. Specifically, we develop and use a model to investigate the impact of decisions caused by a public employment authority that selectively supports job-seekers through targeted help. The selection of who receives what help is based on a data-driven intervention model that estimates an individualâs chances of finding a job in a timely manner and rests upon data that describes a population in which skills relevant to the labor market are unevenly distributed between two groups (e.g., males and females). The intervention model has incomplete access to the individualâs actual skills and can augment this with knowledge of the individualâs group affiliation, thus using a protected attribute to increase predictive accuracy. We assess this intervention modelâs dynamicsâespecially fairness-related issues and trade-offs between different fairness goals- over time and compare it to an intervention model that does not use group affiliation as a predictive feature. We conclude that in order to quantify the trade-off correctly and to assess the long-term fairness effects of such a system in the real-world, careful modeling of the surrounding labor market is indispensable