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 individual’s 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 individual’s actual skills and can augment this with knowledge of the individual’s group affiliation, thus using a protected attribute to increase predictive accuracy. We assess this intervention model’s dynamics—especially 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

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

TIER2 D3.1 - State-of-play on methods, tools, practices to increase reproducibility across diverse epistemic contexts

This project deliverable has been submitted to, but not yet reviewed by the Research Executive Agency, and might thus be subject to change. TIER2, over the course of its three-year duration (2023-2025), aims to contribute to improving this situation in various ways. Key to our approach is to centre “epistemic diversity” (defined below) by selecting three broad research areas — social, life, and computer sciences, and two cross- disciplinary stakeholder groups of research publishers and funders — to systematically investigate the roles, nature, and meanings of reproducibility across contexts. Through coordinated co- creation with these communities, TIER2 aims to boost knowledge on reproducibility, create tools, engage communities, implement interventions and policy across different contexts to increase reproducibility where it is relevant. This Deliverable details work to provide the theoretical, evidential and strategic framework for the project. The aim is to capture the complexity in the meaning(s) of reproducibility across contexts, provide a conceptual framework that systematically relates epistemic diversity to reproducibility by identifying key research characteristics affecting the relevance and feasibility of different types of reproducibility, establish current levels of knowledge on which interventions work in which contexts (including in two specific cross-cutting research methods (qualitative and Machine Learning-driven research), and devise a strategic intervention logic for designing and implementing interventions that aim at sustainable behavioural change towards increased reproducibility. This work has been addressed through seven ambitious individual studies: • “Definitions of reproducibility” (Section 2) • “Epistemic diversity and Knowledge Production Modes” (Sec. 3) • “Scoping review and evidence mapping of interventions aimed at improving reproducible and replicable science” (Sec. 4) • “Review of conceptions and facilitators of and barriers to reproducibility of qualitative research” (Sec. 5) • “Review of conceptions and practices regarding reproducibility in Machine Learning (ML)- driven research” (Sec. 6) • “Changing behaviour in the academy: A strategy for improving research culture and practice” (Sec. 7) This work hence fills knowledge gaps to enable the mapping of “impact pathways”, i.e., the possible paths that connect input to output, outcome and impact (including linkages of causal mechanisms and drivers/barriers), to elucidate the routes to increased reproducibility across diverse contexts. This work is crucial to inform the future stages of TIER2, especially to design, implement and test a series of new tools and instruments (the “pilots”) conducted within TIER2 Work Packages 4 and 5

TIER2 - Enhancing Trust, Integrity and Efficiency in Research through next-level Reproducibility

Lack of reproducibility of research results has become a major theme in recent years. TIER2 is an international project funded by the European Commission under their Horizon Europe programme. Covering three broad research areas (social, life and computer sciences) and two cross-disciplinary stakeholder groups (research publishers and funders) to systematically investigate reproducibility across contexts, TIER2 will significantly boost knowledge on reproducibility, create tools, engage communities, implement interventions and policy across different contexts to increase re-use and overall quality of research results in the European Research Area and global R&I, and consequently increase trust, integrity and efficiency in research