COVID-19 Safety


It is our priority to ensure the safest possible in-person ICWSM. For this, we strongly encourage:

  1. Wearing a mask indoors if not presenting or eating
  2. Wearing a mask while traveling to ICWSM
  3. Being fully vaccinated or a negative test result
  4. Monitoring symptoms and attending virtually if you suspect you have COVID-19
  5. Socializing outdoors as much as possible (banquet will be outdoors)

Let's protect ourselves and those most vulnerable among us by taking these easy precautions!


ICWSM Code of Conduct


For inquiries about the code of conduct, please contact icwsm22@aaai.org.


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.


Disciplines

Computational approaches to social media research including:
  • Natural Language Processing
  • Text / Data Mining
  • Machine Learning
  • Image / Multimedia Processing
  • Graphics and Visualization
  • Distributed Computing
  • Graph Theory and Network Science
  • Human-computer Interaction
Social science approaches to social media research including:
  • Psychology
  • Sociology and social network analysis
  • Communication
  • Political Science
  • Economics
  • Anthropology
  • Media Studies and Journalism
  • Digital Humanities
  • Interdisciplinary approaches to social media research combining computational algorithms and social science methodologies

Topics Include (But Not Limited To)

  • Studies of digital humanities (culture, history, arts) using social media
  • Psychological, personality-based and ethnographic studies of social media
  • Analysis of the relationship between social media and mainstream media
  • Qualitative and quantitative studies of social media
  • Centrality/influence of social media publications and authors
  • Ranking/relevance of social media content and users
  • Credibility of online content
  • Social network analysis; communities identification; expertise and authority discovery
  • Trust; reputation; recommendation systems
  • Human computer interaction; social media tools; navigation and visualization
  • Subjectivity in textual data; sentiment analysis; polarity/opinion identification and extraction, linguistic analyses of social media behavior
  • Text categorization; topic recognition; demographic/gender/age identification
  • Trend identification and tracking; time series forecasting
  • Measuring predictability of real world phenomena based on social media, e.g., spanning politics, finance, and health
  • New social media applications; interfaces; interaction techniques
  • Engagement, motivations, incentives, and gamification.
  • Social innovation and effecting change through social media
  • Social media usage on mobile devices; location, human mobility, and behavior
  • Organizational and group behavior mediated by social media; interpersonal communication mediated by social media

Example Data Sources (Web and Social Media)

  • Social networking sites (e.g., Facebook, LinkedIn)
  • Microblogs (e.g., Twitter, Tumblr)
  • Wiki-based knowledge sharing sites (e.g., Wikipedia)
  • Social news sites and websites of news media (e.g., Huffington Post)
  • Forums, mailing lists, newsgroups
  • Community media sites (e.g., YouTube, Flickr, Instagram)
  • Social Q&A sites (e.g., Quora, Yahoo Answers)
  • User reviews (e.g., Yelp, Amazon.com)
  • Social curation sites (e.g., Reddit, Pinterest)
  • Location-based social networks (e.g., Foursquare)

ICWSM-2022 Keynotes

The importance of multiple languages and multiple cultures in NLP research

Alice Oh

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.


Perceived Gender and Political Persuasion: A Social Media Field Experiment during the 2020 Democratic National Primary

Chris Bail

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.

Attending ICWSM-2022

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


Click on the link below to book your reservations: 16th International Conference on Web & Social Media Reservations Link

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.

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. 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.

Registration

ICWSM-22 Registration

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.

ICWSM-22 Registration Fees

Early Registration Deadline: May 17

In-Person:
  • $625 AAAI Member
  • $360 AAAI Student Member
  • $735 Nonmember
  • $420 Nonmember Student

In-Person Silver: Includes discounted conference registration, plus a one-year online new or renewed membership in AAAI.
  • $724 Silver Regular
  • $409 Silver Student

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

Workshop / Tutorials

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.

In-Person:
  • $75 Regular
  • $50 Student

Virtual:
  • $45 Regular
  • $25 Student

Registration / Proof of Student Status

Students will be required to submit proof of student status during the registration process.

Refund Requests

The deadline for refund requests is May 17, 2022. All refund requests must be made in writing to AAAI at icwsm22@aaai.org. A $100.00 processing fee will be assessed for all refunds.


Visa Information

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.

Workshops Schedule

Full-Day Workshops:

International Workshop on Cyber Social Threats (CySoc 2022)

Ugur Kursuncu, Kaicheng Yang, Francesco Pierri, Matthew DeVerna, Megan Squire, Jeremy Blackburn, Yelena Mejova


International Workshop on Social Sensing (SocialSens 2022): Special Edition on Belief Dynamics

Kristina Lerman, Christian Lebiere, Ivan Garibay, Yanbing Mao


Workshop on Images in Online Political Communication (PhoMemes)

Jungseock Joo, Andreu Casas, Cody Buntain, Dhavan Shah, Erik Bucy, Zacharcy Steinert-Threlkeld


Workshop on Novel Evaluation Approaches for Text Classification Systems on Social Media (NEATCLasS)

Björn Ross, Roberto Navigli, Agostina Calabrese


Workshop on Social Media for Emergency Response (SoMER)

Hemank Lamba, Ayan Mukhopadhyay, Alejandro Jaimes



Half-Day Workshops:

Workshop on Data for the Wellbeing of Most Vulnerable

Kyriaki Kalimeri, Yelena Mejova, Daniela Paolotti, Rumi Chunara


Workshop on News Media and Computational Journalism (MEDIATE)

Elena Kochkina, Panayiotis Smeros, Jeremie Rappaz, Marya Bazzi, Maria Liakata, and Arkaitz Zubiaga


Data Challenge

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.


Important Dates

  • Data Challenge opens: Feb 15th, 2022
  • Paper Submission Deadline: April 1, 2022
  • Data Challenge notification: May 1, 2022
  • ICWSM Data Challenge Full-day Workshop: June 6, 2022


Eshwar Chandrasekharan, Mirian Redi, and Savvas Zannettou
(ICWSM-2022 Data Challenge Chairs | data.challenge@icwsm.org)

Science Slam

The sixth ICWSM Science Slam will take place on Monday 6 June, 2020 (workshop day) at 8pm @ Rocky Mountain Pizza.

What is a Science Slam?

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:


  1. The topic of the slam has to be related to social media or data science
  2. Presentations should not take up more than 8 minutes

Why should I attend?

  1. Because you will never have a better chance to listen to the most engaging talks at ICWSM
  2. Because you get to choose the winner
  3. To listen to this great line-up! (Your name here!! Submit a proposal!)

Why should I present at the ICWSM Science Slam?

  1. Because people will think you are awesome
  2. Because there will be lots of beer to fuel the brain of your audience to help them deeply engage in your research
  3. Because you could become the epic winner chosen by the audience!

I'm in! What do I have to do to present?

Send an email to clio@gatech.edu 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.

Where is the location?

Rocky Mountain Pizza
1005 Hemphill Ave NW
Atlanta, GA 30318


(404) 876-8600
https://www.rockymountainpizza.com/
Location: https://goo.gl/maps/3PPyZxiJq6424wgEA


How funny are the ICWSM Science Slams?

Check out the videos of previous ICWSM Science Slams at the Youtube channel:

https://www.youtube.com/channel/UC8XWl28yw8e_Uv7uJYe00YQ

Tutorials Schedule

Using the Twitter API v2 for academic research

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.


Foundations of Machine Learning and Predictive Modeling for the Non-expert

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.


Deep Learning For Human Mobility: Data, Models, and Challenges

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.

Organizers:

  1. Massimiliano Luca is a Ph.D. Student at Bruno Kessler Foundation, Trento, Italy and at the Free University of Bolzano, Bolzano, Italy with 10+ publications at journals and conferences. He is broadly interested in computational social science and machine learning methods to predict and generate human mobility. Currently, he is working on creating geographically transferable deep learning models for human mobility.
  2. Luca Pappalardo is a permanent researcher at the Institute of Information Science and Technologies at the National Research Council (ISTI-CNR), Pisa, Italy. He has worked in the data science field since 2011, with 70+ publications at conferences and journals. He is an expert in the study of human mobility and in particular on the discovery of human mobility patterns and the design of generative mobility models.
  3. Bruno Lepri leads the Mobile and Social Computing Lab (MobS) at Fondazione Bruno Kessler (Trento, Italy). He has recently launched the Center for Computational Social Science and Human Dynamics, a joint initiative between Fondazione Bruno Kessler and the University of Trento. In 2010 he won a Marie Curie Cofund postdoc fellowship and he has held a postdoc position at MIT Media Lab. He holds a Ph.D. in Computer Science from the University of Trento. His research interests include computational social science, machine learning, network science, and personal data management. His research has received attention from several international press outlets and obtained the 10-year impact award at MUM 2021, the James Chen Annual Award for best 2016 UMUAI paper, and the best paper award at ACM Ubicomp 2014. His work on personal data management was one of the case studies discussed at the World Economic Forum.
  4. Gianni Barlacchi is a Machine Learning Scientist in Alexa AI. He has worked in the machine learning field and human mobility since 2014. His expertise concerns theoretical and applied machine learning in the areas of human mobility and natural language processing, he focused on developing machine learning models and neural networks to predict people's activities from textual and geo-spatial data.


BERT for Social Sciences and Humanities

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.

Organizers:

  1. Maria Antoniak is a PhD candidate in Information Science at Cornell University. Her research focuses on unsupervised natural language processing methods and applications to computational social science and cultural analytics. Her work translates methods from natural language processing to insights about communities and self-disclosure by modeling personal experiences shared in online communities. She has a master's degree in computational linguistics from the University of Washington and a bachelor's degree in humanities from the University of Notre Dame, and she has completed research internships at Microsoft, Facebook, Twitter, and Pacific Northwest National Laboratory.
  2. Melanie Walsh is an Assistant Teaching Professor in the iSchool at the University of Washington, where she teaches data science, data ethics, and digital humanities. Previously, she was a Postdoctoral Associate in Information Science at Cornell University. She received her PhD in English & American Literature from Washington University in St. Louis. Her research interests include data science, digital humanities, cultural analytics, social media, and American literature & culture—preferably all of the above combined.
  3. David Mimno is an associate professor in the department of Information Science at Cornell University. He holds a PhD from UMass Amherst and was previously the head programmer at the Perseus Project at Tufts and a researcher at Princeton University. His work is supported by the Sloan foundation and the NSF.
  4. Matthew Wilkens is associate professor of information science at Cornell University. He works on large-scale literary and cultural history and on computational approaches to social dynamics.


Information Extraction for Social Science Research

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.


Trustworthy Business Decision Making Methodologies in Web and Social Media

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.

Organizers:

  1. Xue Hu is a staff Data Scientist at Twitter. She works with multiple product teams including search and the relevance platform. Xue specializes in user behavioral measurement using innovative experimentation designs, observational causal inference approaches, and in building frameworks to guide tradeoffs between short- and long-run impacts. Xue received her Ph.D degree in Economics from UCLA.
  2. Wutao Wei is a data science manager at Twitter. His team focuses on using machine learning to automate agent requests and improve customer support. He has been working on the data science insights and causal inference during his career at Twitter. He got his Ph.D degree in Statistics at Purdue University.


Misinformation and Disinformation in Social Media: Where we are and the Path Ahead

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.

Organizers:

  1. Prasenjit Mitra is a professor in the College of Information Sciences and Technology; The Pennsylvania State University. His current research interests are in the areas of artificial intelligence, applied machine learning, natural language processing, big data analytics, and visual analytics especially in application areas such as social media, medical informatics, wildlife informatics, sports analytics, etc. In the past, he has contributed to the areas of data interoperation, data cleaning, and digital libraries especially in tabular data extraction, information retrieval, and citation recommendation. He received his Ph.D. from Stanford University, where he investigated issues related to modeling data and the semantics of data in an information integration system.
  2. Shreya Ghosh is a postdoctoral fellow with the College of Information Sciences and Technology; The Pennsylvania State University. Her current research interest is understanding public opinion and perception leveraging NLP algorithms from social media data. She received her PhD degree from IIT Kharagpur, India in computer science emphasizing on human movement behavioural pattern mining for time-critical applications.


Controlling for Text in Causal Inference with Double Machine Learning

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.


Using Word Embeddings to Measure Group and Temporal Differences in Meaning

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.


Scholarships & Grants

ICWSM Scholarships

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.


Student Travel Grant (On-site)

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.


Virtual Grant for Underrepresented Groups

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 scholarships@icwsm.org


ICWSM 2022 Student Travel Grant and Virtual Grant Application form (Deadline: May 1): https://umich.qualtrics.com/jfe/form/SV_4TsX8zhwAEj4tf0

ACM Women Scholarship

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


ICWSM Student Volunteer Program

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 scholarships@icwsm.org


ICWSM 2022 Student Volunteer Application form (Deadline: April 20): https://umich.qualtrics.com/jfe/form/SV_cYG9meaYZmtDbN4

ICWSM Awards

ICWSM Adamic-Glance Distinguished Young Researcher Award

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.


Nomination Process and Eligibility

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.


Eligibility Criteria

The award is open to individuals who:


  1. Have received their PhD within the past 7 years. Career interruptions and other special circumstances will be considered and should be mentioned in the nomination justification document.
  2. Perform research that is recognizably within the broad field of social computing/computational social science in terms of their thematic and methodological approaches.
  3. Have published their research in the top venues for social computing or computational social science, including active participation in ICWSM.

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.


Selection Process

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:


  1. Nominator’s information (name, affiliation, email, a link to their webpage).
  2. Nominee’s information (name, affiliation, email, year of PhD, a link to their webpage that contains additional information, for example their CV/resume).
  3. A one-page statement explaining why the nominee deserves the award in question, especially highlighting the novelty and strength of their contributions in the area of computational social science / social computing, and providing evidence of their academic and societal impact. A second page can be included to contain information about career interruption or any special considerations.
  4. Citations for up to three representative publications and/or links to other artefacts documenting the contribution or impact.

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: adamic-glance-award@icwsm.org.


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.


2022 Awards Committee

  • Brooke Foucault Welles (Northeastern University)
  • Emre Kiciman (Microsoft Research)
  • Eni Mustafaraj (Wellesley College)
  • Jason R.C. Nurse (University of Kent)
  • Lexing Xie (Australian National University)

Contact the committee: adamic-glance-award@icwsm.org


Award Ceremony

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.