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This paper contributes to the research on combining public opinion surveys and social media data by a) analyzing the effects of social desirability on the willingness to provide social media account information in surveys, and b) evaluating the congruence of opinions expressed in the survey and on social media. We analyze these questions by considering the willingness to make a sacrifice for the environment, i.e., the willingness to pay higher taxes and higher prices. Our results show that Facebook users who oppose environmental measures are less likely to share their account information in the survey, whereas this effect could not be found among Twitter users. Considering the congruence of opinions expressed in the survey and on Twitter, we find similar tendencies both at the aggregate and the individual level.

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 appreciate recommendations of the same song multiple times. Thus, accounting for users' relistening behavior is critical for music recommender systems. In this paper, we describe a psychology-informed approach to model and predict music relistening behavior that is inspired by studies in music psychology, which relate music preferences to human memory. We adopt a well-established psychological theory of human cognition that models the operations of human memory, i.e., Adaptive Control of Thought-Rational (ACT-R). In contrast to prior work, which uses only the base-level component of ACT-R, we utilize five components of ACT-R, i.e., base-level, spreading, partial matching, valuation, and noise, to investigate the effect of five factors on music relistening behavior: (i) recency and frequency of prior exposure to tracks, (ii) co-occurrence of tracks, (iii) the similarity between tracks, (iv) familiarity with tracks, and (v) randomness in behavior. On a dataset of 1.7 million listening events from Last.fm, we evaluate the performance of our approach by sequentially predicting the next track(s) in user sessions. We find that recency and frequency of prior exposure to tracks is an effective predictor of relistening behavior. Besides, considering the co-occurrence of tracks and familiarity with tracks further improves performance in terms of R-precision. We hope that our work inspires future research on the merits of considering cognitive aspects of memory retrieval to model and predict complex user behavior., Comment: Accepted for publication in RecSys'21 late-breaking results

Polarization of public opinion is a major issue for societies, as high levels can promote adverse effects such as hostility. The present paper focuses on the polarization of opinions regarding COVID-19 prevention measures in survey data and on Twitter in the German-speaking regions of Germany, Austria, and Switzerland. The level of polarization is measured by dispersion and bimodality in the opinions based on the sentiment in Twitter data and the agreement in the survey data. Our paper, however, goes beyond existing research as we consider data from both sources separately and comparatively. For this purpose, we matched individuals’ survey responses and tweets for those respondents who shared their Twitter account information. The analyses show that vaccination is more polarizing compared to mask wearing and contact tracing in both sources, that polarization of opinions is more pronounced in the survey data compared to the Twitter data, but also that individuals’ opinions about the COVID-19 measures are consistent in both sources. We believe our findings will provide valuable insights for integrating survey data and Twitter data to investigate opinion polarization.