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 fifteenth 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-2021 was held virtually from June 8 - 10, with a dedicated program of contributed talks, posters, and demos, and a dedicated day of workshops and tutorials held on June 7th, 2021.
Social Science and Sociophysics Track
We will be continuing the 'social science and sociophysics' track at ICWSM-2021 following its successful debut in 2013. This option is for researchers in social science and sociophysics who wish to submit works without publication in the conference proceedings. While papers in this track will not be published, we expect these submissions to describe the same high-quality and complete work as the main track submissions. Papers accepted to this track will be presented either as full-length or poster presentations integrated with the conference, and their abstracts will be published in the conference proceedings. Papers submitted to this track will be reviewed through the same reviewing process as full papers.
Ella Haig, Kenny Joseph, and Afra Mashhadi
(ICWSM-2021 PC Chairs | email@example.com)
Poster papers must be no longer than 5 pages, with page 5 containing nothing but references, and demo descriptions must be no longer than 3 pages, with page 3 containing nothing but references, and all must be submitted by the deadlines given above.
Ella Haig, Kenny Joseph, and Afra Mashhadi
(ICWSM-2021 PC Chairs | firstname.lastname@example.org)
Dataset Paper Format:
Dataset paper submissions must comprise two parts: a dataset or group of datasets, and metadata describing the content, quality, structure, potential uses of the dataset(s), and methods employed for data collection. Descriptive statistics may be included in the metadata (more sophisticated analyses should be part of a regular paper submission).
Authors need to include a discussion about ethical considerations related to the collection and use of their datasets. Also, authors are encouraged to include a description of how they intend to make their datasets FAIR, and we would encourage authors to consider addressing the questions covered in the Datasheets for Datasets recommendations. Datasets and metadata must be published using a dataset sharing service (e.g. Zenodo, datorium, dataverse, or any other dataset sharing services that indexes your dataset and metadata and increases the re-findability of the data) that provides a DOI for the dataset, which should be included in the dataset paper submission. Dataset paper review will be single blind, and all datasets have to be identified and uploaded at the time of submission. Dataset paper submissions must be between 2-10 pages long and will be part of the full proceedings. All papers must follow AAAI formatting guidelines. For submission guidelines, please refer to the guidelines. We also request that authors submit a small sample of the dataset (maximum of 10MB) to aid the reviewers. This should be submitted as supplementary material on the Precision Conference system.
Duncan Hodges, Dong Nguyen, Savvas Zannettou
(ICWSM-2021 Data Chairs | email@example.com)
The ICWSM-2021 Committee invites proposals for Workshops Day at the 15th International AAAI Conference on Weblogs and Social Media (ICWSM-2021). The Workshops Day will be held on June 7th, 2021. Workshop participants will have the opportunity to meet and discuss issues with a selected focus -- providing an informal setting for active exchange among researchers and developers from a wide range of disciplines, including social science and computer science. Workshops are an excellent forum for exploring emerging approaches and task areas, bridging gaps between the social sciences and computing, and elucidating results of exploratory research.
Members of all section-dividers of the social media research community are encouraged to submit proposals. To foster interaction and exchange of ideas, the workshops will be kept small, with up to 40 participants.
The format of workshops will be determined by their organizers. The two main criteria for the selection of the workshops will be the following:
Workshop organizers who want to publish the papers from their workshop (or significant portions of it) will have the opportunity to do so through workshop proceedings by the AAAI Press. For a list of last year's workshops see https://www.icwsm.org/2020/index.html#workshops_schedule.
Proposals for workshops should be no more than five (5) pages in length (10pt, single column, with reasonable margins), written in English, and should contain the following:
Please email your proposal in a single file to the workshop chairs at firstname.lastname@example.org before the deadline. For additional information please contact the workshop chairs at the same address.
(All deadlines are on 23:59:59 Anywhere on Earth)
Oana Balalau, Katherine Ognyanova, and Daniel Romero
(ICWSM-2021 Workshop Chairs | email@example.com)
ICWSM-2021 invites proposals for Tutorials Day at the 15th International AAAI Conference on Web and Social Media (ICWSM). ICWSM-2021 is seeking proposals for tutorials on topics related to the analysis and understanding of social phenomena in the following themes:
We welcome tutorials of various lengths (including 45 minutes, 90 minutes, or a half day). We are looking for contributions from experts in both the social and computational sciences, in industry, academia, and beyond. For a list of tutorials from previous years, we encourage you to visit the tutorials page for 2018, 2019 and 2020.
The format will be entirely determined by the tutorial organizers—i.e., you! Proposals will be selected for inclusion considering the following criteria:
Proposals of tutorials presented at past events are allowed, although novelty is a plus.
Proposals for tutorials should be no more than three pages in length. Please try to use AAAI Author Kit to format your submission; the author kit is available at http://www.aaai.org/Publications/Templates/AuthorKit19.zip. However, using the AAAI Author Kit is not strictly required. Proposal submissions should include the following information:
Proposals should be submitted in PDF to the submission email (firstname.lastname@example.org). Pre-submission questions can be sent to the tutorials chairs (Jisun An, Sauvik Das and Shaomei Wu) at the same address (email@example.com).
(All deadlines are on 23:59:59 Anywhere on Earth)
Jisun An, Sauvik Das, and Shaomei Wu
(ICWSM-2021 Tutorial Chairs | firstname.lastname@example.org)
ICWSM-2021 is hosting its second data challenge to bring together researchers from across disciplines to solve societally-relevant problems together as a community. This will be enabled by fostering collaboration and exchange of ideas in a structured setting.
For more details, please visit the ICWSM-2021 Data Challenge Website.
Ghita Mezzour, Kai Shu, and Gianluca Stringhini
(ICWSM-2021 Data Challenge Chairs | email@example.com)
Format: Papers must be in high resolution PDF format, formatted for US Letter (8.5" x 11") paper, using Type 1 or TrueType fonts. Full papers are recommended to be 8 pages and must be at most 10 pages long, including references and any appendix. Revision papers and final camera-ready full papers can be up to 12 pages. Dataset papers must be no longer than 10 pages, Poster papers must be no longer than 4 pages, and Demo descriptions must be no longer than 2 pages, and all must be submitted by the deadlines given above, and formatted in AAAI two-column, camera-ready style (templates can be found at https://www.aaai.org/Publications/Templates/AuthorKit21.zip). No source files (Word or LaTeX) are required at the time of submission for review; only the PDF file is permitted. Finally, the copyright slug may be omitted in the initial submission phase and no copyright form is required until a paper is accepted for publication.
Anonymity: ICWSM-2021 review is double-blind. Therefore, please anonymize your submission: do not put the author(s) names or affiliation(s) at the start of the paper, and do not include funding or other acknowledgments in papers submitted for review. References to authors' own prior relevant work should be included, but should not specify that this is the authors' own work. Citations to the author's own work should be anonymized, if possible, or can be added later to the final camera-ready version for publication. It is up to the authors' discretion how much to further modify the body of the paper to preserve anonymity. The requirement for anonymity does not extend outside of the review process, e.g. the authors can decide how widely to distribute their papers over the Internet before the program committee meeting. Even in cases where the author's identity is known to a reviewer, the double-blind process will serve as a symbolic reminder of the importance of evaluating the submitted work on its own merits without regard to authors' reputation. Note that 2-page demo submissions and the dataset paper submissions, and only these, are exempt from the anonymization requirement as they often contain system URLs or URLs to data sharing services.
Language: All submissions must be in English.
Revisions: Papers that were previously submitted to ICWSM and received a "Revise and Resubmit " decision should be accompanied by a copy of the previous reviews and an author response statement. The response statement may be in any format, but many reviewers appreciate a response that begins with an overall summary and then includes a table, with each row containing a reviewer comment in the left cell, and author's response in the right cell. The response cell may explain why no changes were made, or may describe changes and direct the reviewer to a particular page, section, or figure, where the revised content appears. At the discretion of the Senior PC member handling the paper, the revised version may be sent back to some or all of the original reviewers for comment and evaluation, and may also be sent to additional reviewers.
Resubmission: Authors will need to declare if a previous version of their submission was rejected at any peer-reviewed venue, and, if so, summarize the changes made in the current version and include the original review. Authors of rejected papers from ICWSM may revise and submit their revised papers after 6 months of the date of the last decision, but not before. For example, papers submitted in the Jan round can be resubmitted to the September round (6 months after the decision in March) but not the May round. This decision was made to avoid paper rejections due to lack of time for revisions and to discourage authors from submitting papers that are not ready.
Researchers who wish to submit full papers without publication in the conference proceedings, may designate their submission as 'social sciences and sociophysics (not for publication)'. Submissions must adhere to the formatting and content guidelines above. They will be reviewed according to the same process and criteria as all other full paper submissions. While we will not accept previously published papers, papers submitted as social sciences and sociophysics (not for publication) may be under review concurrently at a journal. Papers accepted to this track will be full presentations, integrated with the conference, but will be published only as abstracts in the ICWSM conference proceedings.
Submissions originally designated as not for publication cannot be converted at the end to publication in the ICWSM conference proceedings, because that would provide a mechanism enabling simultaneous consideration of the same paper for publication in two venues. Researchers who do wish to publish their papers in the ICWSM proceedings should submit to the regular track. All submitted papers, whether targeted for publication or not, will be judged according to the same acceptance criteria.
ICWSM-2021 will not accept any paper that, at the time of submission, is under review for or has already been published or accepted for publication in a journal or conference. This restriction does not apply to submissions for non-archival workshops.
While we will not accept previously published papers, papers submitted as social sciences and sociophysics (not for publication) may be under review concurrently at a journal.
If duplicate submissions are identified during the review process then:
Authors will be contacted about how to register for the conference. General registration for this year’s virtual conference will open soon. Stay tuned!
All accepted papers and extended abstracts will be published in the conference proceedings, except for those submitted to the 'social sciences and sociophysics (not for publication)'; only abstracts will be published for those. Though initial submissions of full papers must not exceed ten (10) pages, full papers accepted for publication will be allocated up to twelve (12) pages in the conference proceedings to facilitate to address comments raised by the reviewers. Authors will be required to transfer copyright to AAAI.
ICWSM provides a service for hosting datasets pertaining to research presented at the conference. Authors of accepted papers will be encouraged to share the datasets on which their papers are based, while adhering to the terms and conditions of the data provider. Of these datasets, one will be selected for an award which will be based on the quality, scope, and timeliness of each dataset. More information will be available on our website.
The main conference will be held on 8th, 9th, and 10th June between 9:00 am - 3:00 pm Eastern Standard Time (2:00 pm to 8:00 pm British Standard Time), with a replay (featuring a mix of recorded and live sessions) for Asia (10:00 am China Standard Time).
Earlier this year, we circulated a survey among present and past authors about your preferences for a virtual conference. Based on the feedback we received from all of you, we have carefully planned this year’s virtual conference to be an accessible and rewarding experience for everyone.
There are two ways of enjoying the conference this year:
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 firstname.lastname@example.org. If you have any concerns or items to report, please reach out to the Virtual Conference Co-Chairs: Eni Mustafaraj, David Schoch and Kokil Jaidka, or to the General Chair: Jason R.C. Nurse.
Online registration is available at https://aaaiconf.cventevents.com/icwsm21
The ICWSM-2021 technical conference registration fee includes admission to the Workshop/Tutorial Day, keynote addresses, technical sessions, poster sessions, and other virtual events.
** IMPORTANT NOTE: ICWSM is pleased to offer residents of South America and Africa a 50% discount on their registration fee. To request a discount code prior to registration, please write to email@example.com. Be sure to provide your full affiliation and address when requesting a discount.
Early registration: May 7, 2021
Late registration: May 28, 2021
Regular AAAI Member: Early, $150 | Late, $170
Student AAAI Member: Early, $75 | Late, $95
Nonmember: Early, $175 | Late, $195
Student Nonmember: Early, $100 | Late, $120
Includes discounted conference registration, plus a one-year online new or renewed membership in AAAI.
Regular Silver: Early, $249 | Late, $269
Student Silver: Early, $124 | Late, $144
Workshop Information: https://www.icwsm.org/2021/index.html#workshops_schedule
Tutorial Information: https://www.icwsm.org/2021/index.html#tutorials_schedule
ICWSM-21 workshops and tutorials will be held June 7, just prior to the technical conference. Technical registrants may sign up for any combination of workshops and/or tutorials on June 7 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
Workshop/Tutorial Day Only Fee
To register online, please complete the form at (https://aaaiconf.cventevents.com/icwsm21). Students will be required to submit proof of student status during the registration process.
The deadline for refund requests is June 3, 2021. All refund requests must be made in writing to AAAI at firstname.lastname@example.org. A $50.00 processing fee will be assessed for all refunds.
ICWSM and AAAI are pleased to announce the availability of a number of scholarships to help support attendance of underrepresented groups and regions at ICWSM-2021. These scholarships were made possible through the engagement with, and kind contribution of, our sponsors. These ICWSM-2021 grants provide complimentary technical program registration for persons from groups and regions traditionally underrepresented at ICWSM and in the field of Computational Social Science research.
There are no conditions for accepting the grant. We only would 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. If you would like to participate in any activities engagement before or during the conference, feel free to let us know this in your application.
To apply for this ICWSM-2021 Grant Program, please complete the online application at the link below no later than May 10, 2021.
Notifications will be sent by May 17, and complimentary registrations will be issued through AAAI.
Inquiries may be directed to email@example.com.
ICWSM-2021 Scholarship Application form: https://aaaiforms.wufoo.com/forms/wkmljyn0999ygr/
ICWSM and AAAI are pleased to announce the availability of a small fund to help support student attendance at ICWSM-2021. ICWSM-2021 student grants provide complimentary technical program registration for students who are 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 organising committee); and submit applications by April 23, 2021. In the event that applications exceed the number of available grant places, we will aim to have a reasonable balance between students who have an accepted technical paper and those who are actively participating in the conference in some way.
As part of the requirements for accepting a grant, students 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.
To apply for the ICWSM-2021 Student Grant Program, please complete the online application at the link below no later than April 23, 2021. Notifications will be sent by April 27, and complimentary registrations will be issued through AAAI.
Inquiries may be directed to firstname.lastname@example.org.
ICWSM-2021 Student Volunteer Application form: https://aaaiforms.wufoo.com/forms/w18de92s1iuamko/
This annual award is presented to a young researcher who has distinguished themself through innovative research in the area of 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 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.
Both self-nominations and nominations 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.
The award is open to individuals who:
The selection committee consists of three or more members and is appointed by the AAAI ICWSM Steering Committee Chair. The committee solicits nominations and self-nominations from the computational social science and social computing 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 behaviour, which may be quantitative or qualitative in nature. Depth and impact are valued over breadth of contribution for this award.
The nomination form asks the following questions:
Addendum: A candidate can be nominated by multiple people. The nomination form reduces the burden for supporting an existing nomination by requiring only the support letter, since other details would have been provided earlier by the first nominator or self-nominator.
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.
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.
The list of governance challenges brought by digital technology is long and touches every aspect of modern life. Platforms such as Amazon, Uber, Google, Facebook, Linkedin, YouTube, and Twitter effectively control access to a wide variety of information, services, and products. Surveillance systems, algorithmic filtering of information, bias, discrimination, social and political rankings of citizens are a few examples of challenges of the digital world.
I will first focus on the structural level to discuss three digital governance problems that are worth highlighting, namely the multistakeholder nature of the Internet, the climate of growing global polarization and the rampant online misinformation. I will also discuss how computing research can stimulate the development of future policies and regulation for the digital world, with novel conceptions of governance using new technologies of distributed systems. This talk will suggest new opportunities to construct the future of digital governance, in a legitimate, inclusive, and secure use of digital resources to produce sustainable services and public policies for the online world.
Virgilio Almeida is a Professor Emeritus of Computer Science at the Federal University of Minas Gerais (UFMG). He is also Faculty Associate at the Berkman Klein Center at Harvard University. Virgilio received his PhD degree in Computer Science at Vanderbilt University, a Master's degree in computer science at PUC-Rio and a bachelor degree in Electrical Engineering from UFMG. He held visiting positions in several universities and research labs, such as Harvard University (School of Engineering and Applied Sciences), New York University, Boston University, Santa Fe Institute HP Labs. Professor Virgilio is the co-author of five books dealing Web technologies, e-commerce, performance modeling and capacity planning, published by Prentice Hall. His most recent book published by Palgrave MacMIllam is, "Governance for the Digital World." Virgilio was one of the commissioners of the Global Commission for the Stability of Cyberspace. His current research interests focus on social computing, governance of algorithms, modeling and analysis of large scale distributed systems.
A growing body of work in algorithmic fairness has brought issues around "data bias" to the forefront. Often, these discussions have focused on ways in which marginalized communities are under- and mis-represented in datasets, contributing to unintended consequences in algorithmic decision-making. In this talk, we argue that these challenges are only a part of a broader set of data inequalities. We present a framework encompassing data exclusion, inclusion, accuracy, usability, access, and ownership. Using examples in criminal justice, public service provisions, and health, we demonstrate ways in which processes in data generation, access, and use create and amplify harms in algorithmic decision-making. We conclude with a discussion on ways to combat these challenges using interventions drawing from a range of disciplines.
Rediet Abebe is an Assistant Professor of Computer Science at the University of California, Berkeley and a Junior Fellow at the Harvard Society of Fellows. Abebe holds a Ph.D. in computer science from Cornell University and graduate degrees in mathematics from Harvard University and the University of Cambridge. Abebe co-founded and co-organizes Mechanism Design for Social Good (MD4SG) and is serving as Program Co-chair for the inaugural ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO '21). Her dissertation received the 2020 ACM SIGKDD Dissertation Award and an honorable mention for the ACM SIGEcom Dissertation Award for offering the foundations of this emerging research area. Abebe's work has informed policy and practice at the National Institute of Health (NIH), the Ethiopian Ministry of Education, and numerous non-profit organizations. Abebe also co-founded Black in AI, a non-profit organization tackling equity issues in AI. Her research is influenced by her upbringing in her hometown of Addis Ababa, Ethiopia.
Overview: Are you new to ICWSM? Are you curious about the year-long submission cycles (May, September, and January deadlines)? Do you want to learn more about what makes for a successful submission for the ICWSM journal? Two of the ICWSM editors-in-chief (Budak and Xie) and two of the 2021 ICWSM program chairs will answer your questions about the publishing process at ICWSM. If you cannot attend the panel in person, please submit your questions here. A recording of the panel will be made available for later viewing.
Overview: ICWSM has always been a conference that has uplifted women researchers and highlighted their excellent contributions to social computing/computational social science. For the 15th anniversary of the conference, we bring together some of the women who have shaped the conference and left their mark in the community. They will have a conversation with the audience about the past, present, and future of social computing. If you cannot attend the panel in person, please submit your questions here. A recording of the panel will be made available for later viewing.
Overview: ICWSM is an international and diverse community. As we seek to grow and mature further, it is important to reflect on where we are now and whether there are opportunities to improve our global equity and diversity efforts. This panel brings together a series of experts within, and outside of, the core ICWSM community to critically reflect on the first 15 years of ICWSM, and how diverse and inclusive the conference has been. With this basis we then discuss how ICWSM can develop in these areas in the future, including identifying key steps to facilitate this global growth. If you cannot attend the panel in person, please submit your questions here. A recording of the panel will be made available for later viewing.
Market Forces: Quantifying the Role of Top Credible Ad Servers in the Fake News Ecosystem
Lia Bozarth, Ceren Budak
Uncovering Coordinated Networks on Social Media: Methods and Case Studies
Diogo Pacheco, Pik-Mai Hui, Christopher Torres-Lugo, Bao Tran Truong, Alessandro Flammini, Filippo Menczer
Context-Based Quotation Recommendation
Ansel MacLaughlin, Tao Chen, Burcu Karagol Ayan, Dan Roth
Posting Bot Detection on Blockchain-based Social Media Platform using Machine Learning Techniques
Taehyun Kim, Hyomin Shin, Hyung Ju Hwang, Seungwon Jeong
The Effect of Moderation on Online Mental Health Conversations
David Wadden, Tal August, Qisheng Li, Tim Althoff
Learning to Classify Morals and Conventions: Artificial Intelligence in Terms of the Economics of Convention
David Solans, Christopher Tauchmann, Aideen Farrell, Karolin Kappler, Hans-Hendrik Huber, Carlos Castillo, Kristian Kersting
Web Routineness and Limits of Predictability: Investigating Demographic and Behavioral Differences Using Web Tracking Data
Juhi Kulshrestha, Marcos Oliveira, Orkut Karaçalık, Denis Bonnay, Claudia Wagner
Group Link Prediction Using Conditional Variational Autoencoder
Hao Sha, Mohammad Al Hasan, George Mohler
Automatic Discovery of Political Meme Genres with Diverse Appearances
William Theisen, Joel Brogan, Pamela Bilo Thomas, Daniel Moreira, Pascal Phoa, Tim Weninger, Walter Scheirer
Textual Analysis and Timely Detection of Suspended Social Media Accounts
Dominic Seyler, Shulong Tan, Dingcheng Li, Jingyuan Zhang, Ping Li
Multilayer Graph Association Rules for Link Prediction
Michele Coscia, Michael Szell
Perceptions of Retrospective Edits, Changes, and Deletion on Social Media
Günce Su Yılmaz, Fiona Gasaway, Blase Ur, Mainack Mondal
X-Posts Explained: Analyzing and Predicting Controversial Contributions in Thematically Diverse Reddit Forums
Anna Guimarães, Gerhard Weikum
Assessing Media Bias in Cross-Linguistic and Cross-National Populations
Allan Sales, Albin Zehe, Leandro Balby Marinho, Adriano Veloso, Andreas Hotho, Janna Omeliyanenko
No Walk in the Park: The Viability and Fairness of Social Media Analysis for Parks and Recreational Policy Making
Afra Mashhadi, Samantha G. Winder, Emilia H. Lia, Spencer A. Wood
Discovering and Categorising Language Biases in Reddit
Xavier Ferrer, Tom van Nuenen, Jose M. Such, Natalia Criado
"Call me sexist, but..." : Revisiting Sexism Detection Using Psychological Scales and Adversarial Samples
Mattia Samory, Indira Sen, Julian Kohne, Fabian Flöck, Claudia Wagner
How-to Present News on Social Media: A Causal Analysis of Editing News Headlines for Boosting User Engagement
Kunwoo Park, Haewoon Kwak, Jisun An, Sanjay Chawla
Variation in Situational Awareness Information due to Selection of Data Source, Summarization Method, and Method Implementation
M. Janina Sarol, Ly Dinh, Jana Diesner
Cannot Predict Comment Volume of a News Article before (a few) Users Read It
Lihong He, Chen Shen, Arjun Mukherjee, Slobodan Vucetic, Eduard Dragut
The Evolution of the Manosphere across the Web
Manoel Horta Ribeiro, Jeremy Blackburn, Barry Bradlyn, Emiliano De Cristofaro, Gianluca Stringhini, Summer Long, Stephanie Greenberg, Savvas Zannettou
Identifying Misinformation from Website Screenshots
Sara Abdali, Rutuja Gurav, Siddharth Menon, Daniel Fonseca, Negin Entezari, Neil Shah, Evangelos E. Papalexakis
Political Depolarization of News Articles Using Attribute-Aware Word Embeddings
Ruibo Liu, Lili Wang, Chenyan Jia, Soroush Vosoughi
MILE: A Multi-Level Framework for Scalable Graph Embedding
Jiongqian Liang, Saket Gurukar, Srinivasan Parthasarathy
Network Inference from a Mixture of Diffusion Models for Fake News Mitigation
Karishma Sharma, Xinran He, Sungyong Seo, Yan Liu
On Predicting Personal Values of Social Media Users using Community-Specific Language Features and Personal Value Correlation
Amila Silva, Pei-Chi Lo, Ee Peng Lim
Embeddings-Based Clustering for Target Specific Stances: The Case of a Polarized Turkey
Ammar Rashed, Mucahid Kutlu, Kareem Darwish, Tamer Elsayed, Cansın Bayrak
Which Node Attribute Prediction Task Are We Solving? Within-Network, Across-Network, or Across-Layer Tasks
Kristen M. Altenburger, Johan Ugander
Discourse Parsing for Contentious, Non-Convergent Online Discussions
Stepan Zakharov, Omri Hadar, Tovit Hakak, Dina Grossman, Yifat Ben-David Kolikant, Oren Tsur
Experience-Driven Peer Effects: Evidence from a Large Natural Experiment
William Cai, Johan Ugander
It's a Thin Line Between Love and Hate: Using the Echo in Modeling Dynamics of Racist Online Communities
Eyal Arviv, Simo Hanouna, Oren Tsur
PopFactor: Live-Streamer Behavior and Popularity
Robert Netzorg, Lauren Arnett, Augustin Chaintreau, Eugene Wu
An Analysis of Replies to Trump's Tweets
Zijian An, Kenneth Joseph
The Healthy States of America: Creating a Health Taxonomy with Social Media
Sanja Šćepanović, Luca Maria Aiello, Ke Zhou, Sagar Joglekar, Daniele Quercia
Understanding the Invitation Acceptance in Agent-initiated Social E-commerce
Fengli Xu, Guozhen Zhang, Yuan Yuan, Hongjia Huang, Diyi Yang, Depeng Jin, Yong Li
Expertise and Dynamics within Crowdsourced Musical Knowledge Curation: A Case Study of the Genius Platform
Derek Lim, Austin R. Benson
Fair Representation Learning for Heterogeneous Information Networks
Ziqian Zeng, Rashidul Islam, Kamrun Naher Keya, James Foulds, Yangqiu Song, Shimei Pan
Deception Detection in Group Video Conversations using Dynamic Interaction Networks
Srijan Kumar, Chongyang Bai, Venkatramanan Siva Subrahmanian, Jure Leskovec
Machine Learning Explanations to Prevent Overtrust in Fake News Detection
Sina Mohseni, Fan Yang, Shiva Pentyala, Mengnan Du, Yi Liu, Nic Lupfer, Xia Hu, Shuiwang Ji, Eric Ragan
Social Facilitation Among Gamblers: A Large-Scale Study Using Account-Based Data
Niklas Hopfgartner, Tiago Santos, Michael Auer, Mark Griffiths, Denis Helic
Political Polarization in Online News Consumption
Kiran Garimella, Tim Smith, Rebecca Weiss, Robert West
You Don't Know How I Feel: Insider-Outsider Perspective Gaps in Cyberbullying Risk Detection
Seunghyun Kim, Afsaneh Razi, Gianluca Stringhini, Pamela J. Wisniewski, Munmun De Choudhury
On the Role of Micro-categories to Characterize Event Popularity in Meetup
Ayan Kumar Bhowmick, Soumajit Pramanik, Sayan Pathak, Bivas Mitra
How Medical Crowdfunding Helps People? A Large-scale Case Study on the Waterdrop Fundraising
Junjie Huang, Huawei Shen, Qi Cao, Li Cai, Xueqi Cheng
Sudden Attention Shifts on Wikipedia During the COVID-19 Crisis
Manoel Horta Ribeiro, Kristina Gligorić, Maxime Peyrard, Florian Lemmerich, Markus Strohmaier, Robert West
Modeling Collective Anticipation and Response on Wikipedia
Ryota Kobayashi, Patrick Gildersleve, Takeaki Uno, Renaud Lambiotte
Cross-Partisan Discussions on YouTube: Conservatives Talk to Liberals but Liberals Don't Talk to Conservatives
Siqi Wu, Paul Resnick
The Media During the Rise of Trump: Identity Politics, Immigration, "Mexican" Demonization and Hate-Crime
Orestis Papakyriakopoulos, Ethan Zuckerman
Misinformation Adoption or Rejection in the Era of COVID-19
Maxwell Weinzierl, Suellen Hopfer, Sanda M. Harabagiu
Understanding the Diverging User Trajectories in Highly-related Online Communities during the COVID-19 Pandemic
Jason Shuo Zhang, Brian Keegan, Qin Lv, Chenhao Tan
Political Bias and Factualness in News Sharing across more than 100,000 Online Communities
Galen Weld, Maria Glenski, Tim Althoff
Measuring Societal Biases from Text Corpora with Smoothed First-Order Co-occurrence
Navid Rekabsaz, Robert West, James Henderson, Allan Hanbury
"I Won the Election!": An Empirical Analysis of Soft Moderation Interventions on Twitter
CEAM: The Effectiveness of Cyclic and Ephemeral Attention Models of User Behavior on Social Platforms
Farhan Asif Chowdhury, Yozen Liu, Koustuv Saha, Nicholas Vincent, Leonardo Neves, Neil Shah, Maarten W. Bos
Under the Spotlight: Web Tracking in Indian Partisan News Websites
Vibhor Agarwal, Yash Vekaria, Pushkal Agarwal, Sangeeta Mahapatra, Shounak Set, Sakthi Balan Muthiah, Nishanth Sastry, Nicolas Kourtellis
Political Discussion is Abundant in Non-political Subreddits (and Less Toxic)
Ashwin Rajadesingan, Ceren Budak, Paul Resnick
Understanding the Use of Fauxtography on Social Media
Yuping Wang, Fatemeh Tahmasbi, Jeremy Blackburn, Barry Bradlyn, Emiliano De Cristofaro, David Magerman, Savvas Zannettou, Gianluca Stringhini
Representation of music creators on Wikipedia, differences in gender and genre
Alice Wang, Aasish Pappu, Henriette Cramer
Global Gender Differences in Wikipedia Readership
Isaac Johnson, Florian Lemmerich, Diego Sáez-Trumper, Robert West, Markus Strohmaier, Leila Zia
Coordinated Behavior on Social Media in 2019 UK General Election
Leonardo Nizzoli, Serena Tardelli, Marco Avvenuti, Stefano Cresci, Maurizio Tesconi
Partisan Responses to Fact-Checking in Online News Platforms: Evidence from a Political Rumor about the North Korean Leader
TaeYoung Kang, Jaeung Sim
Evaluating Audience Loyalty and Authenticity in Influencer Marketing via Multi-task Multi-relational Learning
Seungbae Kim, Xiusi Chen, Jyun-Yu Jiang, Jinyoung Han, Wei Wang
An Embedding-based Joint Sentiment-Topic Model for Short Texts
Ayan Sengupta, William Scott Paka, Suman Roy, Gaurav Ranjan, Tanmoy Chakraborty
Towards Emotion- and Time-Aware Classification of Tweets to Assist Human Moderation for Suicide Prevention
Ramit Sawhney, Harshit Joshi, Alicia Nobles, Rajiv Ratn Shah
User Identity Linkage for Different Behavioral Patterns across Domains
Genki Kusano, Masafumi Oyamada
RAFFMAN: Measuring and Analyzing Sentiment in Online Political Forum Discussions with an Application to the Trump Impeachment
Jakapun Tachaiya, Joobin Gharibshah, Kevin E. Esterling, Michalis Faloutsos
More than meets the tie: Examining the Role of Interpersonal Relationships in Social Networks
Minje Choi, Ceren Budak, Daniel M. Romero, David Jurgens
RecTen: A Recursive Hierarchical Low Rank Tensor Factorization Method to Discover Hierarchical Patterns from Multi-modal Data
Risul Islam, Md Omar Faruk Rokon, Evangelos E. Papalexakis, Michalis Faloutsos
On Positive Moderation Decisions
Online Communication Shifts in the Midst of the Covid-19 Pandemic: A Case Study on Snapchat
Qi Yang, Weinan Wang, Lucas Pierce, Rajan Vaish, Xiaolin Shi, Neil Shah
Exercise? I thought you said 'Extra Fries': Leveraging Sentence Demarcations and Multi-hop Attention for Meme Affect Analysis
Shraman Pramanick, Md Shad Akhtar, Tanmoy Chakraborty
Computational Analysis of Bot Activity in the Asia-Pacific: A Comparative Study of Four National Elections
Joshua Uyheng, Kathleen M. Carley
Understanding the Dynamics between Vaping and Cannabis Legalization Using Twitter Opinions
Shishir Adhikari, Akshay Uppal, Robin Mermelstein, Tanya Berger-Wolf, Elena Zheleva
Multilingual Contextual Affective Analysis of LGBT People Portrayals in Wikipedia
Chan Young Park, Xinru Yan, Anjalie Field, Yulia Tsvetkov
Analysis of Twitter Users' Lifestyle Choices using Joint Embedding Model
Tunazzina Islam, Dan Goldwasser
Imagine All the People: Characterizing Social Music Sharing on Reddit
Veniamin Veselovsky, Isaac Waller, Ashton Anderson
How Metaphors Impact Political Discourse: A Large-Scale Topic-Agnostic Study Using Neural Metaphor Detection
Vinodkumar Prabhakaran, Marek Rei, Ekaterina Shutova
Management responses and gender bias: Evidence from the hotel industry
Davide Proserpio, Isamar Troncoso, Francesca Valsesia
Estimating the impact of Airbnb on the local economy: Evidence from the restaurant industry
Yongseok Kim, Davide Proserpio, Suman Basuroy
Studying Moral-based Differences in the Framing of Political Tweets
Markus Reiter-Haas, Simone Kopeinik, Elisabeth Lex
Classifying Reasonability in Retellings of Personal Events Shared on Social Media: A Preliminary Case Study with /r/AmITheAsshole
Ethan Haworth, Ted Grover, Justin Langston, Ankush Patel, Joseph West, Alex C. Williams
Evolution of Retweet Rates in Twitter User Careers: Analysis and Model
Kiran Garimella, Robert West
Risk-aware Regularization for Opinion-based Portfolio Selection
Ting-Wei Hsu, Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen
A Research Agenda for Financial Opinion Mining
Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen
American Politicians Diverge Systematically, Indian Politicians do so Chaotically: Text Embeddings as a Window into Party Polarization
Amar Budhiraja, Ankur Sharma, Rahul Agrawal, Monojit Choudhury, Joyojeet Pal
Well-Being Depends on Social Comparison: Hierarchical Models of Twitter Language Suggest That Richer Neighbors Make You Less Happy
Salvatore Giorgi, Sharath Chandra Guntuku, Johannes C. Eichstaedt, Claire Pajot, H. Andrew Schwartz, Lyle H. Ungar
VoterFraud2020: a Multi-modal Dataset of Election Fraud Claims on Twitter
Anton Abilov, Yiqing Hua, Hana Matatov, Ofra Amir, Mor Naaman
Tracking Knowledge Propagation Across Wikipedia Languages
Rodolfo Vieira Valentim, Giovanni Comarela, Souneil Park, Diego Sáez-Trumper
Check Mate: Prioritizing User Generated Multi-Media Content for Fact-Checking
Tarunima Prabhakar, Anushree Gupta, Kruttika Nadig, Denny George
Fighting the COVID-19 Infodemic in Social Media: A Holistic Perspective and a Call to Arms
Firoj Alam, Fahim Dalvi, Shaden Shaar, Nadir Durrani, Hamdy Mubarak, Alex Nikolov, Giovanni Da San Martino, Ahmed Abdelali, Hassan Sajjad, Kareem Darwish, Preslav Nakov
YouNiverse: Large-Scale Channel and Video Metadata from English-Speaking YouTube
Manoel Horta Ribeiro, Robert West
CrisisBench: Benchmarking Crisis-related Social Media Datasets for Humanitarian Information Processing
Firoj Alam, Hassan Sajjad, Muhammad Imran, Ferda Ofli
A Dataset of State-Censored Tweets
Tuğrulcan Elmas, Rebekah Overdorf, Karl Aberer
HumAID: Human-Annotated Disaster Incidents Data from Twitter with Deep Learning Benchmarks
Firoj Alam, Umair Qazi, Muhammad Imran, Ferda Ofli
ABOME: A Multi-platform Data Repository of Artificially Boosted Online Media Entities
Hridoy Sankar Dutta, Udit Arora, Tanmoy Chakraborty
CoVaxxy: A Collection of English-Language Twitter Posts About COVID-19 Vaccines
Matthew R. DeVerna, Francesco Pierri, Bao Tran Truong, John Bollenbacher, David Axelrod, Niklas Loynes, Christopher Torres-Lugo, Kai-Cheng Yang, Filippo Menczer, John Bryden
What's Your Value of Travel Time? Collecting Traveler-Centered Mobility Data via Crowdsourcing
Cristian Consonni, Silvia Basile, Matteo Manca, Ludovico Boratto, Andrè Freitas, Tatiana Kovacikova, Ghadir Pourhashem, Yannick Cornet
COVID-19 Coverage By Cable and Broadcast Networks
Ceren Budak, Ashley Muddiman, Yujin Kim, Caroline C. Murray, Natalie J. Stroud
A Dataset of Multidimensional and Multilingual Social Opinions for Malta's Annual Government Budget
Keith Cortis, Brian Davis
Memes, Radicalisation, and the Promotion of Violence on Chan Sites
Blyth Crawford, Florence Keen, Guillermo Suarez-Tangil
Media Cloud: Massive Open Source Collection of Global News on the Open Web
Hal Roberts, Rahul Bhargava, Linas Valiukas, Dennis Jen, Momin M. Malik, Cindy Sherman Bishop, Emily B. Ndulue, Aashka Dave, Justin Clark, Bruce Etling, Robert Faris, Anushka Shah, Jasmin Rubinovitz, Alexis Hope, Catherine D'Ignazio, Fernando Bermejo, Yochai Benkler, Ethan Zuckerman
A Large Open Dataset from the Parler Social Network
Max Aliapoulios, Emmi Bevensee, Jeremy Blackburn, Barry Bradlyn, Emiliano De Cristofaro, Gianluca Stringhini, Savvas Zannettou
SIGNLENS: A Tool for Analyzing People's Polarization Social Relationship Based on Signed Graph Modeling
Junjie Huang, Huawei Shen, Xueqi Cheng
ESG Tracker: Unbiased and Explainable ESG Profile from Real-time Data
Elaheh Momeni, Constantin Fraenkel, Patrick Kiss, Andreas Burgmann
Sanjaya Wijeratne, Jennifer Lee, Horacio Saggion, and Amit Sheth
Ugur Kursuncu, Yelena Mejova, Megan Squire, Jeremy Blackburn, and Amit Sheth
Indira Sen, Katrin Weller, and Fabian Floeck
Kathleen McKeown, Tarek Abdelzaher, Adriana Iamnitchi, and George Mohler
Yelena Mejova, Kyriaki Kalimeri, Daniela Paolotti, Rumi Chunara
Maurice Jakesch, Manon Revel, and Ziv Epstein
Panayiotis Smeros, Jeremie Rappaz, Marya Bazzi, Elena Kochkina, Maria Liakata, and Karl Aberer
Ebrahim Bagheri, Diana Inkpen, Christopher C. Yang, and Fattane Zarrinkalam
Privacy and rights are now vital to the future of data and the economics of the Internet. A fundamental challenge for new digital business is the paradox of data vs user control. Until recently, the dominant internet business model — starting with social media and search platforms — tilted strongly towards profile completion: the more data, the better.
But as the regulatory climate force companies to give control over personal data back to users, existing user data becomes more valuable while new data collection is strongly penalized. This exacerbates the information asymmetry problem of the Internet, where concentration of data among a few companies limits opportunities for innovation.
In this talk, we will introduce Mozilla Rally, a data sharing platform for users and communities. We'll walk through examples of how we're collaborating with different communities to create large-scale studies of online life through collective sharing of browsing data. We'll also talk about our future roadmap and how we hope to engage with research communities.
Social media sites such as Twitter and Facebook have connected billions of people and given the opportunity to the users to share their ideas and opinions instantly. That being said, there are several ill consequences as well such as online harassment, trolling, cyber-bullying, fake news, and hate speech. Out of these, hate speech presents a unique challenge as it is deep engraved into our society and is often linked with offline violence. Social media platforms rely on local moderators to identify hate speech and take necessary action, but with a prolific increase in such content over social media many are turning toward automated hate speech detection and mitigation systems. This shift brings several challenges on the plate, and hence, is an important avenue to explore for the computation social science community.
In this translation style tutorial, we present an exposition of hate speech detection and mitigation in three steps. First, we shall describe the current state of research in the hate speech domain, focusing on different detection and mitigation systems that have developed over time. Next, we shall highlight the challenges that these systems might carry like bias and lack of transparency. The final section will concretize the path ahead, providing clear guidelines for the community working on hate speech and related areas. We shall outline the open challenges and research directions for interested researchers.
Here, we plan to cover the following topics, i) How is hate speech affecting different platforms? ii) Existing dataset, iii) Text-based hate speech systems, iv) User-based hate speech systems, v) How we can mitigate or slow down the process of spread of hate speech, vi) What are the challenges still present in the domain, e.g.: Explainability and bias, Multimodal and multilingual challenges etc, in the tutorial.
For more details, please visit the tutorial website.
Punyajoy Saha is a PhD scholar at the Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur (India). His research interests lie in the nexus of social computing and natural language processing. He is currently involved in developing mitigation algorithms for hate speech in social media. His works are published at major conferences like The Web Conference, AAAI, ECML-PKDD and ICWSM.
Binny Mathew is a PhD scholar at the Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur (India). His research interest lies in computational social science and natural language processing. He is currently interested in solving issues surrounding hate speech in online social media and providing solutions to counter them. His works are published in leading conferences such as The Web Conference, ICWSM, ECML-PKDD, and WebSci.
Mithun Das is a PhD scholar at the Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur (India). His research interests lie in computational social science and natural language processing.
Pawan Goyal is an Associate Professor at the Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur (India). His research interest lies in natural language processing and text mining.
Kiran Garimella is the first IDSS postdoctoral fellow to receive a Hammer Fellowship, pioneers research into the spread of rumours and misinformation on closed platforms such as WhatsApp, a popular encrypted messaging service with millions of users worldwide. Kiran aims to develop technical solutions to such problems by building tools that can collect and analyze massive social media datasets.
Animesh Mukherjee is an Associate Professor at the Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur (India). His research interest lies in natural language processing, information retrieval and AI and ethics.
This tutorial bridges social media research methods with concepts in ethics and privacy. Researchers studying the web and social media find themselves immersed in a domain where data flows freely and is often considered “public,” but that data is also potentially bound by contextual norms and expectations. While many research communities have engaged with the ethics of data collection, sharing, and retention practices, these debates are often less-visible to parts of the data and computational social science communities. In this tutorial, we will engage the concept of contextual integrity to provide space for the community to discuss how ethical risks emerge when engaging with “public” data, and offer practical methods for addressing this risk. This tutorial will bridge ongoing conversations in research ethics about practices of data collection and retention with emerging practices of data and computational social science researchers. Participants will gain practical methods for identifying, tracking, and mitigating ethical harms related to the collection and use of “public” data in web and social media research.
Casey Fiesler is an assistant professor in Information Science at University of Colorado Boulder, where she researches largely in the areas of technology ethics and governance, particularly in the context of social computing.
Katharina Kinder-Kurlanda is Professor of Human Sciences of the Digital at the Digital Age Research Center (D!ARC) at the Alpen-Adria-Universität Klagenfurt in Austria. Her research interests are emerging epistemological concepts for social media and big data; data ethics; privacy, data protection & security; algorithms; casual games; and the Internet of Things.
Jacob Metcalf is a researcher at Data & Society. He studies how data ethics practices are emerging in environments that have not previously grappled with research ethics, such as industry, IRBs, and civil society organizations.
Emmanuel Moss is a researcher with Data & Society and a doctoral candidate in cultural anthropology at the CUNY Graduate Center. He conducts ethnographic research on issues of fairness and accountability in AI systems.
Katie Shilton is an associate professor in Information Studies at University of Maryland. Her research focuses on ethics and policy for the design of information technologies, systems, and collections.
Michael Zimmer is an associate professor in Computer Science at Marquette University. He is a privacy and data ethics scholar whose work focuses on digital privacy and surveillance, internet research ethics, and the broader social & ethical dimensions of emerging technologies.
In today's data-driven world, organizations derive insights from massive amounts of data through large scale statistical machine learning models. However, statistical techniques can be easy to fool with adversarial instances (a neural network can predict a non-extremist as an extremist by mere presence of the word Jihad), which raises question in Data Quality. In high stakes decision making problems, such as cyber social threats, it is highly sensitive to classify a non-extremist as an extremist and vice-versa. Data quality is good if the data possesses adequate domain coverage and the labels contain adequate semantics. For example, is the semantics of an extremist vs. non-extremist vis-a-vis the word Jihad captured in the label (adequate semantics in labels)? Also, are there enough non-extremists with the word Jihad in the training data from the perspective of religion, hate, or ideology? Thus semantic annotation of the data, beyond mere labels attached to data instances, can significantly improve the robustness of model outcomes and ensure that the model has learned from trustworthy, knowledge-guided data standards. It is important to note that the knowledge-guided standards help de-bias the data if specified correctly (contextualized de-biasing extremist behavior data from bias towards the word Jihad). Therefore, in addition to trust in the robustness of outcomes, knowledge guided data creation also enables fair and ethical practices during real-world deployment of machine learning in high stakes decision making. We denote such data as Explainable Data. In this tutorial of type course and case-studies, we detail how to construct Explainable Data using various expert resources and knowledge graphs. All the materials (resources and implementations) presented during the tutorial will be made available on: KIWO-ICWSM, a week before the tutorial. We plan a 90 minute tutorial (Intermediate Level) with 2 breaks (5 mins each).
For more details, please visit the tutorial website.
Prof. Amit Sheth is an Educator, Researcher, and Entrepreneur. He is the founding director of the university-wide Artificial Intelligence Institute at the University of South Carolina (#AIISC). Previously , he was the LexisNexis Ohio Eminent Scholar and the executive director of Ohio Center of Excellence in Knowledge-enabled Computing. He is a Fellow of IEEE, AAAI, and AAAS. He has organized 75+ international events (general/program chair, organization committee chair), 65+ keynotes, given many well-attended tutorials and is among the well-cited computer scientists. He has founded three companies by licensing his university research outcomes, including the first Semantic Web company in 1999 that pioneered technology similar to what is found today in Google Semantic Search and Knowledge Graph. Several commercial products and deployed systems have resulted from his research.
Kaushik Roy is a Ph.D. student at AIISC. He completed his master's in computer science at Indiana University Bloomington and has worked at UT Dallas’s starling lab. His research interests include statistical relational artificial intelligence, sequential decision making, knowledge graphs, and reinforcement Learning. His work is published in reputed conferences in IEEE, KR, AAAI.
Manas Gaur is currently a Ph.D. Student in Artificial Intelligence Institute at the University of South Carolina. He has been Data Science and AI for Social Good Fellow with the University of Chicago and Dataminr Inc. His interdisciplinary research funded by NIH and NSF operationalizes the use of Knowledge Graphs, Natural Language Understanding, and Machine Learning to solve social good problems in the domain of Mental Health, Cyber Social Harms, and Crisis Response. His work has appeared in premier AI and Data Science conferences (CIKM, WWW, AAAI, CSCW), journals in science (PLOS One, Springer-Nature, IEEE Internet Computing), and healthcare-specific meetings (NIMH MHSR, AMIA).
Usha Lokala is a Ph.D. student at AIISC. Her research interests include ontology engineering, knowledge graphs and natural language processing. Her work has been published in reputed conferences and Journals (IEEE, Drug and Alcohol Dependence, WWW, CPDD). Her work on public health addictions won second prize in Opioid Challenge at SBP BRiMS 2018, a computational social science conference.
AAAI Executive Director
AAAI Conference Coordinator
We would also like to thank the following individuals for their efforts:
We would also like to thank the following reviewers for their efforts:
Note: We include reviewers who reviewed at least 4 papers in a year in the PC list and thank the other individuals who reviewed at least one paper separately. A similar criterion is used for inclusion in the SPC list.
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