====================================================== 2nd Workshop on Online Misinformation- and Harm-Aware Recommender Systems ( OHARS 2021)
Co-located with ACM RecSys 2021 ======================================================
Submission deadline: ***29th July 2021 (abstracts by 24th July)***
AIM AND SCOPE =======================
In recent years, there has been an increase in the dissemination of false news, rumors, deception, and other forms of misinformation, as well as abusive language, incitements of violence, harassment, and other forms of hate speech, throughout online platforms. In fact, these unwanted behaviours lead to online harms which have become a serious problem with several negative consequences, ranging from public health issues to the disruption of democratic systems. While these phenomena are widely observed in social media, they affect the experience of users on multiple online platforms.
The COVID-19 pandemic generated an increased need for information as a response to a highly emotional and uncertain situation. In this context, cases of misinformation linked to health recommendations have been reported during the COVID-19 pandemic (for example, different media outlets, and even politicians, recommended consuming hot beverages and chlorine dioxide for preventing the disease), which undermines the individual responses to COVID-19, compromises the efficacy of evidence-based policy interventions, and affects the credibility of scientific expertise with potentially longer-term (and even deadly) consequences. At the same time, actions were demanded to control the "tsunami'' of hate speech which is rife during the COVID-19 pandemic.
Recommender systems play a central role in the process of online information consumption as well as user decision-making by leveraging user-generated information at scale. In this role, they are both affected by different forms of online harms, which hinders their capacity of achieving accurate predictions and, at the same time, become unintended means for their spread and amplification. In their attempt to deliver relevant and engaging suggestions, recommendation algorithms are prone to introduce biases, and further foster phenomena such as filter bubbles, echo chambers and opinion manipulation.
Harnessing recommender systems with misinformation- and harm-awareness mechanisms becomes essential not only to mitigate the negative effects of the diffusion of unwanted content but also to increase the user-perceived quality of recommender systems in a wide range of online platforms, going from social networks to e-commerce sites. Novel strategies like the diversification of recommendations, bias mitigation, model-level disruption, explainability and interpretation, among others, can help users in making informed decisions in the presence of misinformation, hate speech and other forms of online harm.
TOPICS OF INTEREST =======================
The aim of this workshop is to bring together a community of researchers interested in tackling online harms and, at the same time, mitigating their impact on recommender systems. We will seek novel research contributions on misinformation- and harm-aware recommender systems.
In this second edition, the workshop aims at furthering research in recommender systems that can circumvent the negative effects of online harms by promoting the recommendation of safe content and users, with a special interest in research tackling the negative effects of recommending fake or harmful content linked to the COVID-19 crisis.
We solicit contributions in all topics related to misinformation- and harm-aware recommender systems, focusing on (but not limited to) the following list:
- Reducing misinformation effects (e.g. echo chambers, filter bubbles). - Online harms dynamics and prevalence. - Computational models for multi-modal and multi-lingual harm detection and countermeasures. - User/content trustworthiness. - Bias detection and mitigation in data/algorithms. - Fairness, interpretability and transparency in recommendations. - Explainable models of recommendations. - Data collection and processing. - Design of specific evaluation metrics. - The appropriateness of countermeasures for tackling online harms in recommender systems. - Applications and case studies of misinformation- and harm-aware recommender systems. - Mitigation strategies against coronavirus-fueled hate speech and COVID-related misinformation propagation. - Ethical and social implications of monitoring, tackling and moderating online harms. - Online harm engagement, propagation and attacks in recommender systems. - Privacy preserving recommender systems. - Attack prevention in collaborative filtering recommender systems - Quantitative user studies exploring the effects of harm recommendations.
We encourage works focused on mitigating online harms in domains beyond social media, such as effects in collaborative filtering settings, e-commerce platforms, news-media, video platforms (e.g.YouTube or Vimeo) or opinion-mining applications, among other possibilities. Works specifically analyzing any of the previous topics in the context of the COVID-19 crisis are also welcome, as well as works based on social networks other than Twitter and Facebook, such as Tik-Tok, Reddit, Snapchat and Instagram.
SUBMISSION AND SELECTION PROCESS =======================
We will consider five different submission types, all following the new single-column format ACM proceedings format (following the LaTeX or Word template): regular (max 14 pages), short (between 4-8 pages), and extended abstracts (max 2 pages), excluding references. Authors of long and short papers will also be asked to present a poster.
* Research papers (regular or short) should be clearly placed with respect to the state of the art and state the contribution of the proposal in the domain of application, even if presenting preliminary results. Papers should describe the methodology in detail, experiments should be repeatable, and a comparison with the existing approaches in the literature should be made where possible.
* Position papers (regular or short) should introduce novel points of view in the workshop topics or summarize the experience of a researcher or a group in the field.
* Practice and experience reports (short) should present in detail the real-world scenarios that present harm-aware recommender systems. Novel but significant proposals will be considered for acceptance into this category despite not having gone through sufficient experimental validation or lacking a strong theoretical foundation.
* Dataset descriptions (short) should introduce new public data collections that could be used to explore or develop harm-aware recommender systems.
* Demo proposals (extended abstract or poster) should present the details of a prototype recommender system, to be demonstrated to the workshop attendees.
Submissions will be accepted through Easychair: https://easychair.org/conferences/?conf=ohars2021
Each submitted paper will be refereed by three members of the Program Committee, based on its novelty, technical quality, potential impact, insightfulness, depth, clarity, and reproducibility. In order to generate a strong outcome of the workshop, all long and short accepted papers will be included in the Workshop proceedings, provided that at least one of the authors attends the workshop to present the work. Proceedings will be published in a volume, indexed on Scopus and DBLP (tentatively, CEUR).
IMPORTANT DATES =======================
Abstract submission deadline: July 24th, 2021 Paper submission deadline: July 29th, 2021 Author notification: August 21st, 2021 Camera-ready version deadline: September 4rd, 2021
PROGRAM COMMITTEE CHAIRS =======================
Daniela Godoy, ISISTAN Research Institute (CONICET/UNCPBA), Argentina Antonela Tommasel, ISISTAN Research Institute (CONICET/UNCPBA), Argentina Arkaitz Zubiaga, Queen Mary University of London, UK
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