United States

filter bubbles

polarization

user modeling

recommender systems

**ICWSM-2022 will be held from June 6th – 9th in a hybrid format, both in-person in Atlanta, Georgia and online.**

**The Workshop and Tutorial Sessions are accessible to those that registered separately for these sessions. If you registered to attend but are not able to access the these sessions from the [Schedule tab](https://underline.io/events/291/schedule?day=2022-06-06T04%3A00%3A00.000Z), please email icwsm@underline.io
**

The International AAAI Conference on Web and Social Media (ICWSM) is a forum for researchers from multiple disciplines to come together to share knowledge, discuss ideas, exchange information, and learn about cutting-edge research in diverse fields with the common theme of online social media. This overall theme includes research in new perspectives in social theories, as well as computational algorithms for analyzing social media. ICWSM is a singularly fitting venue for research that blends social science and computational approaches to answer important and challenging questions about human social behavior through social media while advancing computational tools for vast and unstructured data.


ICWSM, now in its sixteenth year, has become one of the premier venues for computational social science, and previous years of ICWSM have featured papers, posters, and demos that draw upon network science, machine learning, computational linguistics, sociology, communication, and political science. The uniqueness of the venue and the quality of submissions have contributed to a rapidly growing conference, and a competitive acceptance rate of approximately 20% for full-length research papers published in the proceedings by the Association for the Advancement of Artificial Intelligence (AAAI).


**ICWSM Code of Conduct**

All persons, organizations and entities that attend AAAI conferences and events are subject to the standards of conduct set forth on the [AAAI Code of Conduct for Events and Conferences](https://aaai.org/Conferences/code-of-conduct.php). AAAI expects all community members to formally endorse this code of conduct, and to actively prevent and discourage any undesired behaviors. Everyone should feel empowered to politely engage when they or others are disrespected, and to raise awareness and understanding of this code of conduct. AAAI event participants asked to stop their unacceptable behavior are expected to comply immediately. Sponsors are also subject to this code of conduct in their participation in AAAI events.


Additionally, participants are encouraged to be courteous when sharing screen captures and photographs of conference events. Seek permission when possible and respect requests to take down images if those featured ask. Concerns around code of conduct or inclusion may be sent to icwsm22@aaai.org. If you have any concerns or items to report, please reach out to the General Chairs: Diyi Yang and Yelena Mejova.<br><br>


A ticket is required to attend the event, please use the below URL to register for the event if you have not already done so:

https://aaaiconf.cventevents.com/event/be8c26b0-a76f-4753-babb-de2bd9d2a619/summary

Virtual-Only:
$200 AAAI Member
$125 AAAI Student Member
$225 Nonmember
$150 Nonmember Student

Virtual Silver: Includes discounted conference registration, plus a one-year online new or renewed membership in AAAI.
$299 Silver Regular
$174 Silver Student

Virtual - Workshop and Tutorials:
$45 Regular
$25 Student


Registration Is Required

ICWSM 2022

The International AAAI Conference on Web and Social Media (ICWSM) is a forum for researchers from multiple disciplines to come together to share knowledge, discuss ideas, exchange information, and learn about cutting-edge research in diverse fields with the common theme of online social media. This overall theme includes research in new perspectives in social theories, as well as computational algorithms for analyzing social media. ICWSM is a singularly fitting venue for research that blends social science and computational approaches to answer important and challenging questions about human social behavior through social media while advancing computational tools for vast and unstructured data.

Recommender systems are central to modern online platforms, but a popular concern is that they may be pulling society in dangerous directions (e.g., towards filter bubbles). However, a challenge with measuring the effects of recommender systems is how to compare user outcomes under these systems to outcomes under a credible counterfactual world without such systems. We take a model-based approach to this challenge, introducing a dichotomy of process models that we can compare: (1) a "recommender" model describing a generic item-matching process under a personalized recommender system and (2) an "organic" model describing a baseline counterfactual where users search for items without the mediation of any system. Our key finding is that the recommender and organic models result in dramatically different outcomes at both the individual and societal level, as supported by theorems and simulation experiments with real data. The two process models also induce different trade-offs during inference: regularization improves the mean squared error of matches in both settings, but at the cost of less diverse (less radical) items chosen in the recommender model but more diverse (more radical) items chosen in the organic model. These findings provide a formal framework for how recommender systems may be fundamentally altering how we search for and interact with content, in a world increasingly mediated by such systems.

To Recommend or Not? A Model-Based Comparison of Item-Matching Processes

technical paper

Local News Online and COVID in the U.S.: Relationships among Coverage, Cases, Deaths, and Audience

Measuring Alignment of Online Grassroots Political Communities with Political Campaigns

Unraveling Social Perceptions & Behaviors towards Migrants on Twitter

On the Infrastructure Providers that Support Misinformation Websites

Cross-Platform Multimodal Misinformation: Taxonomy, Characteristics and Detection for Textual Posts and Videos

Identification of Twitter Bots Based on an Explainable Machine Learning Framework: The US 2020 Elections Case Study

Characterizing Retweet Bots: The Case of Black Market Accounts

The Effect of People Recommenders on Echo Chambers and Polarization

Linguistic Characterization of Contentious Topics Online: Case Studies on Controversy in Abortion, Climate Change, and Gun Control

Divided We Rule: Influencer Polarization on Twitter During Political Crises in India

https://assets.underline.io/transcript/34107/subtitle/191e3ba8925288c14708c8d83cfc5912.vtt

English (automatic)

To Recommend or Not? A Model-Based Comparison of Item-Matching Processes

Downloads

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Local News Online and COVID in the U.S.: Relationships among Coverage, Cases, Deaths, and Audience

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