Call for Papers
2nd Workshop on Online Misinformation- and Harm-Aware Recommender Systems (
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
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
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
- 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
- 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
- Applications and case studies of misinformation- and harm-aware
- Mitigation strategies against coronavirus-fueled hate speech and
COVID-related misinformation propagation.
- Ethical and social implications of monitoring, tackling and moderating
- 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
Submissions will be accepted through Easychair:
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).
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
For more information do not hesitate to contact us: [log in to unmask]
T: +54 249 4385650 interno 2301
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Campus Universitario, Paraje Arroyo Seco.
Tandil, Buenos Aires, Argentina
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