Towards Analyzing the Bias of News Recommender Systems Using Sentiment and Stance Detection

News recommender systems are used by online news providers to alleviate information overload and to provide personalized content to users. However, algorithmic news curation has been hypothesized to create filter bubbles and to intensify users' selective exposure, potentially increasing their vulnerability to polarized opinions and fake news. In this paper, we show how information on news items' stance and sentiment can be utilized to analyze and quantify the extent to which recommender systems suffer from biases. To that end, we have annotated a German news corpus on the topic of migration using stance detection and sentiment analysis. In an experimental evaluation with four different recommender systems, our results show a slight tendency of all four models for recommending articles with negative sentiments and stances against the topic of refugees and migration. Moreover, we observed a positive correlation between the sentiment and stance bias of the text-based recommenders and the preexisting user bias, which indicates that these systems amplify users' opinions and decrease the diversity of recommended news. The knowledge-aware model appears to be the least prone to such biases, at the cost of predictive accuracy

NeMig -- A Bilingual News Collection and Knowledge Graph about Migration

News recommendation plays a critical role in shaping the public's worldviews through the way in which it filters and disseminates information about different topics. Given the crucial impact that media plays in opinion formation, especially for sensitive topics, understanding the effects of personalized recommendation beyond accuracy has become essential in today's digital society. In this work, we present NeMig, a bilingual news collection on the topic of migration, and corresponding rich user data. In comparison to existing news recommendation datasets, which comprise a large variety of monolingual news, NeMig covers articles on a single controversial topic, published in both Germany and the US. We annotate the sentiment polarization of the articles and the political leanings of the media outlets, in addition to extracting subtopics and named entities disambiguated through Wikidata. These features can be used to analyze the effects of algorithmic news curation beyond accuracy-based performance, such as recommender biases and the creation of filter bubbles. We construct domain-specific knowledge graphs from the news text and metadata, thus encoding knowledge-level connections between articles. Importantly, while existing datasets include only click behavior, we collect user socio-demographic and political information in addition to explicit click feedback. We demonstrate the utility of NeMig through experiments on the tasks of news recommenders benchmarking, analysis of biases in recommenders, and news trends analysis. NeMig aims to provide a useful resource for the news recommendation community and to foster interdisciplinary research into the multidimensional effects of algorithmic news curation.Comment: Accepted at the 11th International Workshop on News Recommendation and Analytics (INRA 2023) in conjunction with ACM RecSys 202

Deliverable D4.4 Simulated coherent scattering data from plasma and non–plasma samples

Deliverable D4.4 of work package 4 (SIMEX) in EUCALL

Towards analyzing the bias of news recommender systems using sentiment and stance detection

News recommender systems are used by online news providers to alleviate information overload and to provide personalized content to users. However, algorithmic news curation has been hypothesized to create filter bubbles and to intensify users' selective exposure, potentially increasing their vulnerability to polarized opinions and fake news. In this paper, we show how information on news items' stance and sentiment can be utilized to analyze and quantify the extent to which recommender systems suffer from biases. To that end, we have annotated a German news corpus on the topic of migration using stance detection and sentiment analysis. In an experimental evaluation with four different recommender systems, our results show a slight tendency of all four models for recommending articles with negative sentiments and stances against the topic of refugees and migration. Moreover, we observed a positive correlation between the sentiment and stance bias of the text-based recommenders and the preexisting user bias, which indicates that these systems amplify users' opinions and decrease the diversity of recommended news. The knowledge-aware model appears to be the least prone to such biases, at the cost of predictive accuracy.Comment: Accepted at the 2nd International Workshop on Knowledge Graphs for Online Discourse Analysis (KnOD 2022) collocated with The Web Conference 2022 (WWW'22), 25-29 April 2022, Lyon, Franc

Divided by the algorithm? The (limited) effects of content- and sentiment-based news recommendation on affective, ideological, and perceived polarization

Recent rises in political polarization across the globe are often ascribed to algorithmic content filtering on social media, news platforms, or search engines. The widespread usage of news recommendation systems (NRS) is theorized to drive users in homogenous information environments and, thereby, drive affective, ideological, and perceived polarization. To test this assumption, we conducted an online experiment (n = 750) with running algorithms that enriches content-based NRS with negative or neutral sentiment. Our experiment finds only limited evidence for polarization effects of content-based NRS. Nevertheless, the time spent with an NRS and its recommended articles seems to play a crucial role as a moderator of polarization. The longer participants were using an NRS enriched with negative sentiment, the more they got affectively polarized, whereas participants using an NRS incorporating balanced sentiment ideologically depolarized over time. Implications for future research are discussed

Bi-allelic variants in CELSR3 are implicated in central nervous system and urinary tract anomalies

CELSR3 codes for a planar cell polarity protein. We describe twelve affected individuals from eleven independent families with bi-allelic variants in CELSR3. Affected individuals presented with an overlapping phenotypic spectrum comprising central nervous system (CNS) anomalies (7/12), combined CNS anomalies and congenital anomalies of the kidneys and urinary tract (CAKUT) (3/12) and CAKUT only (2/12). Computational simulation of the 3D protein structure suggests the position of the identified variants to be implicated in penetrance and phenotype expression. CELSR3 immunolocalization in human embryonic urinary tract and transient suppression and rescue experiments of Celsr3 in fluorescent zebrafish reporter lines further support an embryonic role of CELSR3 in CNS and urinary tract formation.</p

Bi-allelic variants in CELSR3 are implicated in central nervous system and urinary tract anomalies

CELSR3 codes for a planar cell polarity protein. We describe twelve affected individuals from eleven independent families with bi-allelic variants in CELSR3. Affected individuals presented with an overlapping phenotypic spectrum comprising central nervous system (CNS) anomalies (7/12), combined CNS anomalies and congenital anomalies of the kidneys and urinary tract (CAKUT) (3/12) and CAKUT only (2/12). Computational simulation of the 3D protein structure suggests the position of the identified variants to be implicated in penetrance and phenotype expression. CELSR3 immunolocalization in human embryonic urinary tract and transient suppression and rescue experiments of Celsr3 in fluorescent zebrafish reporter lines further support an embryonic role of CELSR3 in CNS and urinary tract formation.</p

"A renewed sense of purpose": mothers' and fathers' experience of having a child following a recent stillbirth.

Most research has focused on mothers' experiences of perinatal loss itself or on the subsequent pregnancy, whereas little attention has been paid to both parents' experiences of having a child following late perinatal loss and the experience of parenting this child. The current study therefore explored mothers' and fathers' experiences of becoming a parent to a child born after a recent stillbirth, covering the period of the second pregnancy and up to two years after the birth of the next baby. In depth interviews were conducted with 7 couples (14 participants). Couples were eligible if they previously had a stillbirth (after 24 weeks of gestation) and subsequently had another child (their first live baby) who was now under the age of 2 years. Couples who had more than one child after experiencing a stillbirth and those who were not fluent in English were excluded. Qualitative analysis of the interview data was conducted using Interpretive Phenomenological Analysis. Five superordinate themes emerged from the data: Living with uncertainty; Coping with uncertainty; Relationship with the next child; The continuing grief process; Identity as a parent. Overall, fathers' experiences were similar to those of mothers', including high levels of anxiety and guilt during the subsequent pregnancy and after the child was born. Coping strategies to address these were identified. Differences between mothers and fathers regarding the grief process during the subsequent pregnancy and after their second child was born were identified. Despite difficulties with bonding during pregnancy and at the time when the baby was born, parents' perceptions of their relationship with their subsequent child were positive. Findings highlight the importance of tailoring support systems not only according to mothers' but also to fathers' needs. Parents', and particularly fathers', reported lack of opportunities for grieving as well as the high level of anxiety of both parents about their baby's wellbeing during pregnancy and after birth implies a need for structured support. Difficulties experienced in bonding with the subsequent child during pregnancy and once the child is born need to be normalised