It is our priority to ensure the safest possible in-person ICWSM. For this, we strongly encourage:
Let's protect ourselves and those most vulnerable among us by taking these easy precautions!
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-2022 will be held from June 6th – 9th in a hybrid format, both in-person in Atlanta, Georgia and online.
Abstract: Among the thousands of human languages used throughout the world, NLP researchers have so far focused on only a handful. This is understandable from the perspective that resources and researchers are not readily available for all languages, but nevertheless it is a profound limitation of our research community, one that must be addressed. I will discuss research on Korean and other low- to medium-resource languages and share the interesting findings that extend beyond the linguistic differences. I will share our work on ethnic bias in BERT language models in six different languages which particularly illustrates the importance of studying multiple languages. I will describe our efforts in building a benchmark dataset for Korean and the main challenge of building the dataset when the sources of data are much smaller compared to English and other major languages. I will also present our latest findings with historical documents written in ancient Korean. Finally, I will share some preliminary results of working with non native speakers who can potentially contribute to research in low-resource languages. Through this talk, I hope to inspire NLP researchers, myself included, to actively engage in a diverse set of languages and cultures.
Bio: Alice Oh is a Professor in the School of Computing at KAIST. She received her PhD in 2008 from MIT and joined KAIST in the same year. Her major research area is at the intersection of machine learning and computational social science. Within machine learning, she studies various models designed for analyzing written text including social media posts, news articles, and personal conversations. She also looks at non-textual data such as social network friendship and logs from online games for which she interacts closely with social scientists for an interdisciplinary approach to computational social science. A particular application focus of applying computational methods to a social science problem is computer science education. Her students have developed a Web-based system for improving programming education, and through that system they collect and analyze large-scale, fine-grained student behavior data. With that data, they aim to understand the behaviors of students and teaching assistants via machine learning models such that they can offer identification of students in need of assistance, provide automatic assistance for simple problems, track students’ progress, and help students to learn better through social learning.
Abstract: Women have less influence than men in many decision-making settings. We evaluate possible sources of this gender gap using a field experiment conducted during the 2020 presidential primary. We paid Democrats to discuss their preferred candidates with another voter on a social media platform that we created called UniteDem. Within these conversations, we randomly assigned some respondents to appear to their partners using gendered avatars that did not match their gender identity. We find that misrepresenting a man as a woman undermines his influence on his partner’s candidate preferences. However, misrepresenting a woman as a man does not significantly increase her relative influence. Additionally, we find evidence of gender differences in word use. The results indicate that both gender stereotypes and differences in speech contribute to a gender gap in interpersonal influence, suggesting that changes to a woman’s behavior or a man’s perception alone will not improve inequalities in women’s influence.
Bio: Chris Bail is Professor of Sociology and Public Policy at Duke University, where he directs the Polarization Lab. He studies political tribalism, extremism, and social psychology using data from social media and tools from the emerging field of computational social science. A Guggenheim Fellow and Carnegie Fellow, Chris's writing appears in leading outlets such as Science, Nature, and the New York Times. His 2015 book, Terrified: How Anti-Muslim Fringe Organizations Became Mainstream, received three awards and resulted in an invitation to address the 2016 Democratic National Convention. His widely acclaimed 2021 book, Breaking the Social Media Prism, was featured in the New York Times, the New Yorker, and described as “masterful,” and "immediately relevant" by Science Magazine. He is the Editor of the Oxford University Press Series in Computational Social Science and the Co-Founder of the Summer Institutes in Computational Social Science. He also serves on the Advisory Committee to the National Science Foundation's Social Behavioral and Economic Sciences Directorate.
The 16th International Conference on Web & Social Media will be hosted at the Georgia Tech Hotel and Conference Center on June 6th through June 9th, 2022. Please reserve before May 6, 2022.
Georgia Tech Hotel and Conference Center
800 Spring St NW
Atlanta, Georgia 30308
Non-smoking King bedding accommodations have been blocked for this group. Please note that all guestrooms are non-smoking. For any other requests or inquiries, please enter this information within the appropriate request boxes during the reservations process or call the hotel directly by calling (800) 706-2899 or (404) 838-2100.
For any additional nights needed before or after the posted group dates, please contact the hotel directly at (800)706-2899 to check availability.
For those attendees driving to the hotel, overnight parking is $21 per night with unlimited in and out access to the garage.
For more information about the venue, transportation, and conference logistics, see here.
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. 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 email@example.com. If you have any concerns or items to report, please reach out to the General Chairs: Diyi Yang and Yelena Mejova.
Online registration is now available!
In-person Registration: https://aaaiconf.cventevents.com/icwsm22inperson
Virtual Registration: https://aaaiconf.cventevents.com/icwsm22virtual
The ICWSM-22 technical conference registration fee includes admission to the Workshop/Tutorial Day, all technical sessions, and access to the electronic version of the ICWSM-22 Conference Proceedings. Please note there are two sets of fees. If you plan to attend in-person, please choose the in-person fees and registration. If you plan to attend virtual-only, please choose the virtual-only fees and registration.
Early Registration Deadline: May 17
ICWSM-22 workshops and tutorials will be held June 6, just prior to the technical conference. Please also note that some sessions may only be offered virtually. Technical registrants may sign up for any combination of workshops and/or tutorials on June 6 as part of their technical registration. For those wishing to attend only the Workshop/Tutorial Day, a Workshop/Tutorial Day Only registration is offered. PARTICIPANTS SHOULD NOT SIGN UP FOR CONCURRENT EVENTS, so please consult the schedule carefully before making your selections.
Students will be required to submit proof of student status during the registration process.
The deadline for refund requests is May 17, 2022. All refund requests must be made in writing to AAAI at firstname.lastname@example.org. A $100.00 processing fee will be assessed for all refunds.
Letters of invitation for visa application purposes can be requested at this link. Please gather the information below. Your request will be answered in the order it is received, Monday – Friday, 8:30 AM – 5:00 PM PT.
Ugur Kursuncu, Kaicheng Yang, Francesco Pierri, Matthew DeVerna, Megan Squire, Jeremy Blackburn, Yelena Mejova
Kristina Lerman, Christian Lebiere, Ivan Garibay, Yanbing Mao
Jungseock Joo, Andreu Casas, Cody Buntain, Dhavan Shah, Erik Bucy, Zacharcy Steinert-Threlkeld
Björn Ross, Roberto Navigli, Agostina Calabrese
Hemank Lamba, Ayan Mukhopadhyay, Alejandro Jaimes
Kyriaki Kalimeri, Yelena Mejova, Daniela Paolotti, Rumi Chunara
Elena Kochkina, Panayiotis Smeros, Jeremie Rappaz, Marya Bazzi, Maria Liakata, and Arkaitz Zubiaga
ICWSM-2022 is hosting the third ICWSM data challenge with the goal of bringing together researchers to analyze and understand emerging societal issues. The data challenge is a space where researchers can exchange ideas, discuss ongoing work, and foster collaboration, grounded on open data. This year’s data challenge theme is Health-Related Discourse on the Web.
For more details, please visit the ICWSM-2022 Data Challenge Website.
Eshwar Chandrasekharan, Mirian Redi, and Savvas Zannettou
(ICWSM-2022 Data Challenge Chairs | email@example.com)
A Science Slam is an epic scientific event where scientists compete with short talks about research. It's just like a poetry slam, but with science instead of poems. Slammers are completely free to do whatever they want on stage, everything is allowed including slides, games, the more creative, the better! The only two rules are:
Send an email to firstname.lastname@example.org with a short proposal (one paragraph) of your topic for the slam before June 1, 2022. Please use the subject “science slam”. We encourage you to submit!
At the event, we will vote on the best slammer candidates, based on (i) the scientific quality, on (ii) the novelty of the topic, and on (iii) the potential for giving an engaging talk.
Rocky Mountain Pizza
1005 Hemphill Ave NW
Atlanta, GA 30318
Check out the videos of previous ICWSM Science Slams at the Youtube channel:https://www.youtube.com/channel/UC8XWl28yw8e_Uv7uJYe00YQ
Abstract: In this tutorial, researchers will learn how to work with the Twitter API to get Twitter data for research. We will first learn what the Twitter API is and look at example research done with it. Next, we will learn how to scope your data for research and how to download your dataset using Twitter's downloader tool. Finally, we will learn how to write code in Python to connect to the Twitter API and get data at scale for your research. We will also learn best practices for curating datasets and sharing with peers.
Organizer: Suhem Parack is a Staff Developer Advocate for Academic Research at Twitter and helps students and researchers understand how to get Twitter data for their research.
Abstract: This tutorial covers the fundamental concepts of machine learning including methods and algorithms for predictive modeling and data driven analysis (k nearest neighbors, k-means clustering, tree-based models, generalized linear model, gradient descent, artificial neural networks, deep learning, principal component analysis, singular value decomposition). The content is designed for the diverse audience of ICWSM and particularly those without a computer science background. Computer or programming background is not needed, but an interactive tutorial on basic Python will be sent to the participants before the workshop so that we can start on an equal footing. No software installation will be needed. Coding will be done on Google Colab https://colab.research.google.com/ The aim of the tutorial is to provide participants with an engaging and interactive experience to learn the fundamentals of machine learning in a jargon-free class to gain critical skills which have started to become increasingly relevant to the predictive analysis of social media data. The tutorial is structured with three 50-minute modules.
Organizer: Samin Aref is an educator and researcher working as an assistant professor, teaching stream at the University of Toronto. Previously, he has worked as a research scientist at the Max Planck Institute for Demographic Research. Samin holds a Ph.D. in computer science from the University of Auckland and an M.Sc. in Industrial Engineering and Operations Research from Sharif University. His areas of research and teaching are Network Science, Machine Learning, Data Science, Operations Research, and Computational Social Science.
Abstract: Urban population is increasing strikingly and human mobility is becoming more complex and bulky, affecting societal aspects such as the spreading of viral diseases (e.g., the COVID-19 pandemics), public and private transportation, well-being, and the quality of the environment. The proliferation of digital mobility data, such as phone records, GPS traces, and social media posts, combined with the outstanding predictive power of AI, triggered the application of deep learning to human mobility. Currently, the literature is mainly focusing on three mobility-related tasks: next-location prediction, which is predicting an individual's future locations; crowd flow prediction, which is forecasting flows on a geographic region; and trajectory generation, i.e., generating realistic individual trajectories. In this tutorial, we provide the audience with: (i) an introduction to the fundamental concepts of human mobility, such as trajectories, flows, tessellations, and mobility patterns; (ii) a review of mobility data sources and common public datasets; (iii) a definition of next-location, crowd flow prediction, and trajectory generation, and a discussion of why they are relevant and challenging problems; (iv) a description of peculiarities and limitations of the deep learning approaches to these three problems, with practical examples on how to train and use them; (v) a discussion about relevant open technical challenges and promising research directions. On the one hand, our tutorial is a guide to the leading deep learning solutions for people already working on human mobility. At the same time, it helps AI scientists and practitioners understand the fundamental concepts and the open challenges of the study of human mobility. Our tutorial is based on our recent review paper, available at https://arxiv.org/abs/2012.02825.
Abstract: In this interactive tutorial, we will introduce participants to large language models that are now common in natural language processing (NLP). We will focus on variants of the popular Bidirectional Encoder Representations from Transformers (BERT) model (Devlin et al., 2018). This family of pre-trained models performs well across a wide range of NLP tasks, but their use poses challenges for researchers in other disciplines. This tutorial will highlight opportunities for social media researchers, from the humanities and social sciences, to take advantage of these large models. Participants will gain hands-on experience with downloading and setting up a pre-trained model, using BERT to analyze words in context, adapting or “fine-tuning” a BERT model to perform better on a curated dataset, and using the fine-tuned model for classification tasks. We will also discuss practical details, like how to run these large models using free resources and which open libraries to use. Most importantly, we will discuss nuances of these models that are most relevant for researchers outside of NLP, including example use cases and exploratory uses of these models; limits to these methods and common errors; using datasets of varying sizes, including small, curated collections; and data processing and tokenization choices.
Abstract: This workshop provides an interactive introduction to information extraction for social science–techniques for identifying specific words, phrases, or pieces of information contained within documents. It focuses on two common techniques, named entity recognition and dependency parses, and shows how they can provide useful descriptive data about the civil war in Syria. It concludes with a brief application of question-answering models for social science information extraction.
Organizer:Andy Halterman is a Faculty Fellow in the NYU Center for Data Science and an incoming assistant professor of political science at Michigan State University. His research develops new computational and natural language processing techniques for social scientists. He holds a PhD from MIT.
Abstract: Online controlled experiments are the gold standard for tech companies decision making and feature iterations. However, in a social media setting, experimenters often face unique challenges including inconsistency between short-term and long-term results, correlated impacts between content producer and consumers, and/or external validation of online experiment results. These issues make it difficult to interpret and understand experiment results, and create hurdles to leverage them to make informed decisions. There are multiple data science tools and causal inference frameworks which can be applied to improve scientific rigor for the decision making process. In this tutorial, we will first review the causal inference methodologies. Then we will introduce double machine learning, surrogate modeling and meta analysis framework with some Twitter’s applications. Finally there will be a hands-on interactive session for the audience to learn how to apply those techniques in their research.
Abstract: This tutorial will address the problem of false information and its propagation in media. In order to take a holistic view, on the one end, we need to look at the very related problem of misinformation and disinformation in newspapers in the early 20th century and how society evolved to eradicate the most egregious of its forms and learned to live with it. On the other end, we will survey the promising technological solutions that have been designed in the last 5-10 years and a bit. The issue of digital literacy will be examined and efforts to teach our school-children how to determine the trustworthiness of information will be discussed. Solutions involving trustworthy third-parties and digital signatories will be evaluated. Time permitting, we will demonstrate these issues studying a few cases and demonstrations. We will also look at governmental policies that have been designed to curb this problem. Finally, an agenda for future work will be discussed.
Abstract: Text plays an increasingly important role in the study of causal relationships. In this tutorial, we consider the specific case of using text as a control to eliminate bias from confounders operating through the text. We formalize the problem of controlling for text using causal graphs and the potential outcomes framework, describe principled estimation and inference procedures to realize this goal using dou-ble/debiased machine learning, and compare this procedure (hands-on) against several alternatives such as controlling for low-dimensional representations of the text obtained via topic modeling, principal component analysis, or other techniques. We conclude with a case study on using text as a control to quantify the causal impact of status on persuasion online.
Organizer:Emaad Manzoor is an assistant professor at the University of Wisconsin Madison. He designs randomized and quasi-experiments to quantify the determinants of persuasion in text-based communication. He received his PhD from Carnegie Mellon University.
Abstract: Word embeddings — representation of words as vectors such that words with similar meanings share geometrical properties — have become standard instruments in a text analyst's toolkit. Two types of representations have gained popularity: a single vector per word invariant to context (non-contextual embeddings) or multiple vectors per word that are sensitive to linguistic context (contextual embeddings). This tutorial is aimed to provide a hands-on introduction to both non-contextual and contextual word embeddings. We’ll demonstrate the utility of these embeddings on measuring differences in meaning across groups (e.g., political parties, subreddits, etc) or over time. The tutorial is designed for an audience who wants to get started with using word embeddings in their own research. Through the tutorial, we will give a conceptual overview of these two types of embeddings, highlight the differences between them, and include a deeper examination through code about how they can be used to analyze variation or change in meaning.
Organizer: Sandeep Soni is a Postdoctoral scholar at the University of California, Berkeley. His research interest is broadly in the field of computational linguistics with an emphasis in studying the social aspects of language. He finished his PhD at Georgia Institute of Technology.
We are pleased to announce the availability of a number of scholarships to help support student attendance at ICWSM-22. These scholarships are made possible through the engagement with, and kind contribution of, AAAI and our company sponsors.
The student travel grant (on-site) assists student participants both with travel to Atlanta, Georgia, USA, as well as with conference expenses (such as housing, local transportation to/from the airport, conference registration, etc.). Please note that it intends to subsidize student participation in ICWSM-22, but does not intend to cover all travel and conference expenses. The final amount will vary depending on the cost of the travel, the quantity of money available, and the number and type of applicants. The eligibility of this travel grant includes both active student program enrollment and physical presence at ICWSM-22 or any of the associated workshops.
The virtual grant provides complimentary conference registration for virtual participants who are from underrepresented groups and/or regions. Note that this virtual grant is not limited to students, it is open to anyone from underrepresented groups (e.g., women, persons with disabilities, etc.) and/or regions (e.g., African countries). The waived registration includes both main programs and workshops/tutorials. There are no conditions for accepting this scholarship. We only ask that you attend the event and enjoy the sessions. We would, of course, also be happy if you decide to join our ICWSM and Computational Social Science community.
To apply for ICWSM Student Travel Grant or Virtual Grant, please complete the online application at the link below no later than May 1, 2022. Notifications will be sent by May 7, and complimentary registrations will be issued through AAAI.
Inquiries can be directed to email@example.com
ACM-W scholarship provides support for women undergraduate and graduate students in Computer Science and related programs to attend research Computer Science conferences. Note that the selection of ACM-W scholarship is decided by the ACM-W Scholarship Committee and unrelated to the above two scholarships provided by ICWSM. So, it is possible that students are granted both scholarships from ACM-W and ICWSM. Please refer to the ACM-W application site for more information. The next ACM-W deadline is April 15, 2022.
ACM Women Scholarship Application (Deadline: April 15): https://women.acm.org/scholarships
We seek a limited number of student volunteers for ICWSM 2022, for both on-site and virtual versions. Student volunteers must be full-time undergraduate or graduate students at colleges and universities; have an accepted paper in the conference program or are participating in another way (workshops, demos, engagement with the organizing committee). Student volunteers will be asked to assist ICWSM organizers before and during the conference for a few hours. This may involve helping to arrange or host activities, and support in the planning and execution of conference-related tasks. In exchange, student volunteers will receive free conference registration (both main programs and workshops/tutorials). In the event that applications exceed the number of available places, we will aim to keep the volunteer team diverse in terms of language, geo-location, gender, etc.
To apply for the ICWSM 2022 Student Volunteer Program, please complete the online application at the link below no later than April 20, 2022. Notifications will be sent by April 27, and complimentary registrations will be issued through AAAI.
Inquiries can be directed to firstname.lastname@example.org
ICWSM 2022 Student Volunteer Application form (Deadline: April 20): https://umich.qualtrics.com/jfe/form/SV_cYG9meaYZmtDbN4
This annual award is presented to a young researcher who has distinguished themself through innovative research in the area of social computing/computational social science in the early stage of their independent research career. The award is named after Lada Adamic and Natalie Glance, two outstanding researchers who have made significant contributions to the International AAAI Conference on Web and Social Media (ICWSM) in particular and social computing/computational social science in general. The ICWSM research community at large has greatly impacted this field, through identifying the connections between online digital behaviors and critical societal questions and issues. From misinformation and fake news to how we can use social media and social networks to gain insight into political polarization, mental health, and social movements, the range of topics addressed by the community is continuously expanding. We want to recognize and celebrate the young researchers who are making these contributions today.
The award was established in 2021, at the 15th anniversary mark of the AAAI ICWSM conference.
The inaugural awardee is Dr. Tanu Mitra.
Self-nominations, nominations, and letters of support are elicited.. ICWSM strongly encourages individuals from underrepresented groups in research (based on gender identity, race, ethnicity, geographical location, etc.) to self-nominate, and urges the wide community to nominate young researchers who have distinguished themselves for their creativity and rigor in identifying and addressing important research topics of societal impact. Nominations are open from February 1st to March 1st 2022.
The award is open to individuals who:
As long as a candidate is eligible based on the three criteria above, they will be considered even if they were nominated or self-nominated in prior years.
The selection committee consists of three to five members and is appointed by the AAAI ICWSM Steering Committee Chair. The committee solicits self-nominations, nominations, and letters of support from the social computing/computational social science community. The selection is based on the impact of the candidate's work in the field in identifying significant new problems, creating promising new ideas, paradigms, and tools related to data-driven understanding of human behavior, which may be quantitative or qualitative in nature. Depth and impact are valued over breadth of contribution for this award. A strong regard for considering the ethical aspects of the data/methods used in social computing/computational social science is expected of the research record those nominated.
The nomination form asks the following questions:
Note for letters of support: The form makes it easy to submit letters of support from people other than the nominators or self-nominators. Such individuals will not need to complete the details of the nomination, they will simply upload their letter.
Form accessibility: The nomination form requires Google authentication. If for any reason this is a problem for the nominator, please send the nomination materials via email to: email@example.com.
Conflict of interest: The awards committee takes conflict of interest seriously. If an nominated individual is a former or current collaborator of one or more of the committee members, such member(s) recuse themselves from evaluating and voting on these nominations.
Contact the committee: firstname.lastname@example.org
The award will be presented annually during the AAAI ICWSM conference. Starting in 2022, the awardee will be given the opportunity to give a plenary talk at the conference and announce the new recipient. Each recipient will be listed with a citation for their award on the ICWSM Adamic-Glance Distinguished Young Researcher Award web page. Financial support for attending the conference will be provided.
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