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

Transparent Music Preference Modeling and Recommendation with a Model of Human Memory Theory

<p>Book chapter accepted for the Springer book "A Human-centered Perspective of Intelligent Personalized Environments and Systems"</p><p> </p><p>In this chapter, we discuss how to utilize human memory models for the task of modeling music preferences for recommender systems.</p><p>Therefore, we discuss the theoretical underpinnings of using cognitive models for user modeling and recommender systems in order to introduce a model based on the cognitive architecture ACT-R to predict the music genre preferences of users in the Last.fm platform.</p><p>By implementing the declarative memory module of ACT-R, comprising past usage frequency and recency, as well as the current semantic context, we model the music relistening behavior of users.</p><p>We evaluate our approach using three user groups that we identify in Last.fm, namely (i) low-mainstream music listeners, (ii) medium-mainstream music listeners, and (iii) high-mainstream music listeners.</p><p>We find that our approach provides significantly higher prediction accuracy than various baseline algorithms for all three user groups, and especially for the low-mainstream user group.</p><p>Since our approach is based on a well-established human memory model, we also discuss how this contributes to the transparency of the calculated predictions. </p&gt