32 citations · 50 across the 7 of their papers we have counts for
7 papers
Framing Analysis of Health-Related Narratives: Conspiracy versus Mainstream Media
Markus Reiter-Haas, Beate Klösch, Markus Hadler +1
Understanding how online media frame issues is crucial due to their impact on public opinion. Research on framing using natural language processing techniques mainly focuses on spe…
FrameFinder: Explorative Multi-Perspective Framing Extraction from News Headlines
Markus Reiter-Haas, Beate Klösch, Markus Hadler +1
Revealing the framing of news articles is an important yet neglected task in information seeking and retrieval. In the present work, we present FrameFinder, an open tool for extrac…
mCPT at SemEval-2023 Task 3: Multilingual Label-Aware Contrastive Pre-Training of Transformers for Few- and Zero-shot Framing Detection
Markus Reiter-Haas, Alexander Ertl, Kevin Innerebner +1
This paper presents the winning system for the zero-shot Spanish framing detection task, which also achieves competitive places in eight additional languages. The challenge of the…
Predicting Music Relistening Behavior Using the ACT-R Framework
Markus Reiter-Haas, Emilia Parada-Cabaleiro, Markus Schedl +3
Providing suitable recommendations is of vital importance to improve the user satisfaction of music recommender systems. Here, users often listen to the same track repeatedly and a…
Studying Moral-based Differences in the Framing of Political Tweets
Markus Reiter-Haas, Simone Kopeinik, Elisabeth Lex
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 politicia…
Should we Embed? A Study on the Online Performance of Utilizing Embeddings for Real-Time Job Recommendations
Markus Reiter-Haas, Emanuel Lacic, Tomislav Duricic +2
In this work, we present the findings of an online study, where we explore the impact of utilizing embeddings to recommend job postings under real-time constraints. On the Austrian…
Beyond Accuracy Optimization: On the Value of Item Embeddings for Student Job Recommendations
Emanuel Lacic, Dominik Kowald, Markus Reiter-Haas +2
In this work, we address the problem of recommending jobs to university students. For this, we explore the utilization of neural item embeddings for the task of content-based recom…