United States

Though significant efforts such as removing false claims and promoting reliable sources have been increased to combat COVID-19 ``misinfodemic&#39;&#39;, it remains a societal challenge if lacking a good understanding of susceptible online users, i.e., those who are likely to be attracted by, believe and spread misinformation. This study attempts to tackle this problem by understanding {\it who} constitutes the population vulnerable to the online misinformation in the pandemic, and what are the robust features and short-term behavior signals that distinguish susceptible users from others. Using a 6-month longitudinal user panel on Twitter collected from a geopolitically diverse context in US, we distinguish different types of users, ranging from social bots to humans with various level of engagement with COVID-related misinformation. We then identify users&#39; online features and situational predictors that correlate with their susceptibility to COVID-19 misinformation. This work brings unique contributions: First, contrary to the prior studies on bot influence, our analysis shows that social bots&#39; contribution to misinformation sharing was surprisingly low, and human-like users&#39; misinformation behaviors exhibit heterogeneity. While the sharing of misinformation was highly concentrated, the risk of occasionally sharing misinformation for average users remained alarmingly high. Second, our findings highlight the political sensitivity, activeness and responsiveness to emotionally-charged content among susceptible users. Third, using an interpretable deep learning model, \addr{we demonstrate a feasible solution to efficiently predict users&#39; transient susceptibility solely based on their short-term news consumption and exposure from their networks.} Our methodology and results have an implication in designing effective intervention mechanism to mitigate the misinformation dissipation.

ICWSM 2022

Characterizing User Susceptibility to COVID-19 Misinformation on Twitter

misinformation susceptibility

social bots

covid-19 misinformation

**ICWSM-2022 will be held from June 6th – 9th in a hybrid format, both in-person in Atlanta, Georgia and online.**

**The Workshop and Tutorial Sessions are accessible to those that registered separately for these sessions. If you registered to attend but are not able to access the these sessions from the [Schedule tab](https://underline.io/events/291/schedule?day=2022-06-06T04%3A00%3A00.000Z), please email icwsm@underline.io
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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).


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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.

Though significant efforts such as removing false claims and promoting reliable sources have been increased to combat COVID-19 ``misinfodemic'', it remains a societal challenge if lacking a good understanding of susceptible online users, i.e., those who are likely to be attracted by, believe and spread misinformation. This study attempts to tackle this problem by understanding {\it who} constitutes the population vulnerable to the online misinformation in the pandemic, and what are the robust features and short-term behavior signals that distinguish susceptible users from others. Using a 6-month longitudinal user panel on Twitter collected from a geopolitically diverse context in US, we distinguish different types of users, ranging from social bots to humans with various level of engagement with COVID-related misinformation. We then identify users' online features and situational predictors that correlate with their susceptibility to COVID-19 misinformation. This work brings unique contributions: First, contrary to the prior studies on bot influence, our analysis shows that social bots' contribution to misinformation sharing was surprisingly low, and human-like users' misinformation behaviors exhibit heterogeneity. While the sharing of misinformation was highly concentrated, the risk of occasionally sharing misinformation for average users remained alarmingly high. Second, our findings highlight the political sensitivity, activeness and responsiveness to emotionally-charged content among susceptible users. Third, using an interpretable deep learning model, \addr{we demonstrate a feasible solution to efficiently predict users' transient susceptibility solely based on their short-term news consumption and exposure from their networks.} Our methodology and results have an implication in designing effective intervention mechanism to mitigate the misinformation dissipation.

technical paper

Wikipedia, a collaborative crowd-sourced platform, is the largest, most visited online encyclopedia. Since it spreads information freely in more than 300 languages, many users, tools, and dashboards rely on its content. Hence, there is a need to maintain its fairness and completeness. However, previous research has indicated the existence of a significant gender gap in Wikipedia biographical articles. We already know that a minimal proportion of those articles portray women and there are gender asymmetries in the textual content of these articles, but little has been reported about the visual aspects (e.g., image volume or image quality) of the gender gap. Here, we analyze all biographies available on Wikipedia across 300 occupations in the ten most widely spoken languages, and undertake quantitative and qualitative analysis of gender differences in the written and visual content. The cross-lingual results indicate that (1) much of the male bias in content arises when editors select which personalities should have a Wikipedia page, (2) the trends in written and visual content are quite dissimilar, (3) men biographies tend to have more images across languages, and (4) female biographies average better visual quality. A more granular analysis is performed on English Wikipedia, distinguishing trends in occupations and qualitatively analyzing science and technology biographies. The overall results shed light on the kinds of visual biases that emerge in the collaborative creation
of Wikipedia and yield guidelines for future management of contributions on the platform.

Visual Gender Biases in Wikipedia: A Systematic Evaluation across the Ten Most Spoken Languages

The governance of many online communities relies on rules created by participants. However, prior work provides limited evidence about how these self-governance efforts compare and relate to one another across communities. Studies tend either to analyze communities as discrete entities or consider communities that coexist within a hierarchically-managed platform. In this paper, we investigate both comparative and relational dimensions of self-governance in similar communities. We use exhaustive trace data from the five largest language editions of Wikipedia over almost 20 years since their founding, and consider both patterns in rule-making and overlaps in rule sets. We find similar rule-making activity across the five communities that replicates and extends prior work on English language Wikipedia alone. However, we also find that these Wikipedias have increasingly unique rule sets, even as editing activity concentrates on rules shared between them. Self-governing communities aligned in key ways may share a common core of rules and rule-making practices as they develop and sustain institutional variations.

Rules and Rule-making in the Five Largest Wikipedias

Many applications capture in digital form aspects of people’s lives. The resulting data, which we call Personal Digital Traces - PDTs, can be used to help reconstruct people’s episodic memories and connect to their past personal events. This may have several applications, from helping the recall of patients with neurodegenerative diseases to gathering clues from multiple sources to identify recent contacts and places visited – a critical new application for the current health crisis. This paper takes steps towards integrating and summarizing the heterogeneous collection of data into episodic narratives using scripts - prototypical plans for everyday activities. Specifically, we propose a matching algorithm that groups PDTs from many different sources into script instances (episodes), and we provide a technique for ranking the likelihood of candidate episodes.
We report on the results of a study based on the personal data of real users, which gives evidence that our episode reconstruction 1) integrates well PDTs from different sources into coherent episodes, and 2) augments users’ memory of their past actions.

Supporting Human Memory by Reconstructing Personal Episodic Narratives from Digital Traces

Analyzing gender is critical to study mental health (MH) support in CVD (cardiovascular disease). The existing studies on using social media for extracting MH symptoms consider symptom detection and tend to ignore user context, disease, or gender. The current study aims to design and evaluate a system to capture how MH symptoms associated with CVD are expressed differently with the gender on social media. We observe that the reliable detection of MH symptoms expressed by persons with heart disease in user posts is challenging because of the co-existence of (dis)similar MH symptoms in one post and due to variation in the description of symptoms based on gender. We collect a corpus of 150k items (posts and comments) annotated using the subreddit labels and transfer learning approaches. We propose GeM, a novel task-adaptive multi-task learning approach to identify the MH symptoms in CVD patients based on gender. Specifically, we adopt a knowledge-assisted RoBERTa based bi-encoder model to capture CVD-related MH symptoms. Moreover, it enhances the reliability for differentiating the gender language in MH symptoms when compared to the state-of-art language models. Our model achieves high (statistically significant) performance and predicts four labels of MH issues and two gender labels, which outperforms RoBERTa, improving the recall by 2.14%  on the symptom identification task and by 2.55% on the gender identification  task.

A Computational Approach to Understand Mental Health from Reddit: Knowledge-aware Multitask Learning Framework

Loneliness has been associated with negative outcomes for physical and mental health. Understanding how people express and cope with various forms of loneliness is critical for early screening and targeted interventions to reduce loneliness, particularly among vulnerable groups such as young adults. To examine how different forms of loneliness and coping strategies manifest in loneliness self-disclosure, we built a dataset, FIG-Loneliness (FIne-Grained Loneliness) by using Reddit posts in two young adult-focused forums and two loneliness related forums consisting of a diverse age group. We provide annotations by trained human annotators for binary and fine-grained loneliness classifications of the posts. Trained on FIG-Loneliness, two BERT-based models were used to understand loneliness forms and authors' coping strategies in these forums. Our binary loneliness classification archived an accuracy above 97%, and fine-grained loneliness category classification reached an average accuracy of 77% across all labeled categories. With FIG-Loneliness and model predictions, we found that loneliness expressions in the young adult related forums are distinct from other forums. Those in young adult-focused forums are more likely to express concerns pertaining to peer relationship, and are potentially more sensitive to geographical isolation impacted by the COVID-19 pandemic lockdown. Also, we show that different forms of loneliness have differential use in coping strategies.

Many Ways to be Lonely: Fine-grained Characterization of Loneliness and its Potential Changes in COVID-19

Alzheimer’s disease and related dementia (ADRD) is a collection of disorders involving mental deterioration, which is often quite distressing for the individuals and those caring for them. Online social media platforms have become popular environments for people to share their ADRD caring challenges and experiences. Despite encouraging findings in the literature regarding online support for ADRD caregivers, studies to date have focused only on a single online community about ADRD, which leads to an incomplete picture of the needs of ADRD caregivers. Additionally, the large volume of data from online communities makes it challenging for both researchers and caregivers to efficiently discover discussions about ADRD care. In this paper, we focus on Reddit, an online rating and discussion platform that consists of many communities, or \textit{subreddits}, and aim to analyze the topic difference regarding ADRD care between ADRD and non-ADRD subreddits. To do so, we first develop a two-stage classification framework to extract posts about ADRD care from the entire Reddit. Then, we apply structured topic modeling to investigate what has been discussed on ADRD care and how such discussions are prevalent in different types of subreddits. Our results show that non-ADRD subreddits contribute 68.5\% of submissions of ADRD care, more than twice as many as ADRD subreddits. Moreover, non-ADRD subreddits are more likely to disclose legal and financial issues, negative relationships and mental health, while ADRD subreddits are more likely to talk about memory loss, sleeping and diet issues, the disease and clinical visits. Our findings suggest that research in this area should look into discussions beyond ADRD communities to gain a comprehensive understanding of ADRD caring experiences and challenges.

"Rough Day, Need a Hug": Learning Challenges and Experiences of the Alzheimer's Disease and Related Dementia Caregivers on Reddit

Because of the rapidly growing access of social media platforms, it provides convenient means for users to participate in multiple online activities on various contents and create fast widespread interactions. However, characterizing user types to understand people’s lifestyle decisions shared in social media is challenging task due to diverse information. In this paper, we propose a weakly supervised graph embedding based framework for creating user representation to understand user types. We evaluate the user embedding learned using weak supervision over well-being related tweets from Twitter, focusing on `Yoga’, `Keto diet’.  Experiments on real-world datasets demonstrate that the proposed framework outperforms the baselines for detecting user types. Finally, we illustrate data analysis on different types of users (e.g., practitioner vs. promotional) from our dataset. While we focus on lifestyle-related tweets (i.e., yoga, keto), our method for constructing user representation readily generalizes to other domains.

Twitter User Representation using Weakly Supervised Graph Embedding

Advances in Natural Language Processing (NLP) have revolutionized the way researchers and practitioners address crucial societal problems. Large language models are now the standard to develop state-of-the-art solutions for text detection and classification tasks. However, the development of advanced computational techniques and resources is disproportionately focused on the English language, sidelining a majority of the languages spoken globally. While existing research has developed better multilingual and monolingual language models to bridge this language disparity between English and non-English languages, we explore the promise of incorporating the information contained in images via multimodal machine learning. Our comparative analyses on three detection tasks focusing on crisis information, fake news, and emotion recognition, as well as five high-resource non-English languages, demonstrate that: (a) detection frameworks based on pre-trained large language models like BERT and multilingual-BERT systematically perform better on the English language compared against non-English languages, and (b) including images via multimodal learning bridges this performance gap. We situate our findings with respect to existing work on the pitfalls of large language models, and discuss their theoretical and practical implications.

Overcoming Language Disparity in Online Content Classification with Multimodal Learning

Social media has become an important method for information sharing. This has also created opportunities for bad actors to easily spread disinformation and manipulate public opinion. This paper explores the possibility of applying Authorship Verification on online communities to mitigate abuse by analyzing the writing style of online accounts to identify accounts managed by the same person. We expand on a similarity-based authorship verification approach, previously applied on large fanfictions, and show that it works in open-world settings, shorter documents, and is largely topic-agnostic. Our expanded model can link Reddit accounts based on the writing style of only 40 comments with an AUC of 0.95, and the performance increases to 0.98 given more content. We apply this model on a set of suspicious Reddit accounts associated with the disinformation campaign surrounding the 2016 U.S. presidential election and show that the writing style of these accounts are inconsistent, indicating that each account was likely maintained by multiple individuals. We also apply this model to Reddit user accounts that commented on the WallStreetBets subreddit around the 2021 GameStop short squeeze and show that a number of account pairs share very similar writing styles. We also show that this approach can link accounts across Reddit and Twitter with an AUC of 0.91 even when training data is very limited.

Using Authorship Verification To Mitigate Abuse In Online Communities

Advances in Artificial Intelligence (AI) have made it possible to automate human-level visual search and perception tasks on the massive sets of image data shared on social media on a daily basis. However, AI-based automated filters are highly susceptible to deliberate image attacks that can lead to content misclassification of cyberbulling, child sexual abuse material (CSAM), adult content, and deepfakes. One of the most effective methods to defend against such disturbances is adversarial training, but this comes at the cost of generalization for unseen attacks and transferability across models. In this article, we propose a robust defense against adversarial image attacks, which is model agnostic and generalizable to unseen adversaries. We begin with a baseline model, extracting the latent representations for each class and adaptively clustering the latent representations that share a semantic similarity. Next, we obtain the distributions for these clustered latent representations along with their originating images. We then learn semantic reconstruction dictionaries (SRD). We adversarially train a new model constraining the latent space representation to minimize the distance between the adversarial latent representation and the true cluster distribution. To purify the image, we decompose the input into low and high-frequency components. The high-frequency component is reconstructed based on the best SRD from the clean dataset. In order to evaluate the best SRD, we rely on the distance between the robust latent representations and semantic cluster distributions. The output is a purified image with no perturbations. Evaluations using comprehensive datasets including image benchmarks and social media images demonstrate that our proposed purification approach guards and enhances the accuracy of AI-based image filters for unlawful and harmful perturbed images considerably.

Characterizing User Susceptibility to COVID-19 Misinformation on Twitter

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