United States

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.

ICWSM 2022

&quot;Rough Day, Need a Hug&quot;: Learning Challenges and Experiences of the Alzheimer&#39;s Disease and Related Dementia Caregivers on Reddit

model training

content analysis

alzheimer's

social media

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

technical paper

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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.

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.

Adaptive Clustering of Robust Semantic Representations for Adversarial Image Purification

OpenStreetMap (OSM) is the world’s largest peer-produced geospatial project. As a freely-editable open map of the world to which anyone may contribute or make use of, the dynamics and motivations of its contributors have been the object of significant scholarship. A growing phenomena in the OSM community is the increasing contributions of paid editing teams hired by tech corporations, such as, Microsoft, Apple, and Facebook. Though corporations have long supported OSM in various ways, the recent growth of teams of paid editors raises challenges to the community’s norms and policies, which are historically oriented around contributions by individual volunteer, making it hard to track the contribution of paid editors. This research addresses a fundamental problem in approaching these concerns: understanding the scale and character of corporate editing in OSM. We use machine-learning to improve upon prior approaches to estimating this phenomena, contributing both a novel methodology as well a more robust understanding of the latest corporate editing behavior in OSM.

An Automated Approach to Identifying Corporate Editing

Social reputation (e.g., likes, comments, shares, etc.) on YouTube is the primary tenet to popularize channels/videos. However, the organic way to improve social reputation is tedious, which often provokes content creators to seek services of online blackmarkets for rapidly inflating content reputation. Such blackmarkets act underneath a thriving collusive ecosystem comprising core users and compromised accounts (together known as collusive users). Core users form the backbone of blackmarkets; thus, spotting and suspending them may help in destabilizing the entire collusive network. Although a few studies focused on collusive user detection on Twitter, Facebook, and YouTube, none of them differentiate between core users and compromised accounts.
We are the first to present a rigorous analysis of core users in YouTube blackmarkets. To this end, we collect a new dataset of collusive YouTube users. We study the core-periphery structure of the underlying collusive commenting network (CCN). We examine the topology of CCN to explore the behavioral dynamics of core and compromised users. We then introduce KORSE, a novel graph-based method to automatically detect core users based only on the topological structure of CCN. KORSE performs a weighted k-core decomposition using our proposed metric, called Weighted Internal Core Collusive Index (WICCI). However, KORSE is infeasible to adopt in practice as it requires complete interactions among collusive users to construct CCN. We, therefore, propose NURSE, a deep fusion framework that only leverages user timelines (without considering the underlying CCN) to detect core blackmarket users. Experimental results show that NURSE is quite close to KORSE in detecting core users and outperforms nine baselines.

Weakening the Inner Strength: Spotting Core Collusive Users in YouTube Blackmarket Network

Research into influence campaigns on Twitter has mostly relied on identifying malicious activities from tweets obtained via public APIs. By design, these approaches ignore deleted tweets. However, bad actors can delete content strategically to manipulate the system. Here, we provide the first exhaustive, large-scale analysis of anomalous deletion patterns involving more than a billion deletions by over 11 million accounts. Estimates based on publicly available Twitter data underestimate the true deletion volume. A small fraction of accounts delete a large number of tweets daily. We uncover two abusive behaviors that exploit deletions. First, limits on tweet volume are circumvented, allowing certain accounts to flood the network with over 26 thousand daily tweets. Second, coordinated networks of accounts engage in repetitive likes and unlikes of content that is eventually deleted, which can manipulate ranking algorithms. These kinds of abuse can be exploited to amplify content and inflate popularity, while evading detection. Our study provides platforms and researchers with new methods for identifying social media abuse.

Manipulating Twitter Through Deletions

On social media, due to complex interactions between users' attention and recommendation algorithms, the visibility of users' posts can be unpredictable and vary wildly, sometimes creating unexpected viral events for `ordinary’ users. How do such events affect users' subsequent behaviors and long-term visibility on the platform? For almost 3 years, we collected activities and changes in follower graphs of 17,157 scientists on Twitter, and identified those who experienced `unusual' virality for the first time in their profile lifespan.
Using a matching strategy, we investigated how viral events influence behavioral patterns and follower counts of those who experienced virality (`viral' group) vs. those who didn't.  After virality, the viral group increased tweeting frequency, their tweets became more objective and focused on fewer topics, and expressed more positive sentiment relative to their pre-virality tweets. Also, their post-virality tweets were more aligned with their professional expertise and similar to the viral tweet compared to past tweets. Finally, the viral group gained more followers in short and long terms than their matched counterparts.

The Impact of Viral Posts on Visibility and Behavior of Professionals: A Longitudinal Study of Scientists on Twitter

For AI technology to fulfill its full promises, we must have effective means to ensure Responsible AI behavior and curtail potential irresponsible use, e.g., in areas of privacy protection, human autonomy, robustness, and prevention of biases and discrimination in automated decision making. Recent literature in the field has identified serious shortcomings of narrow technology focused and formalism-oriented research and has proposed an interdisciplinary approach that brings the social context into the scope of study.
In this paper, we take a sociotechnical approach to propose a more expansive framework of thinking about the Responsible AI challenges in both technical and social context. Effective solutions need to bridge the gap between a technical system with the social system that it will be deployed to. To this end, we propose human agency and regulation as main mechanisms of intervention and propose a decentralized computational infrastructure, or a set of public utilities, as the computational means to bridge this gap. A decentralized infrastructure is uniquely suited for meeting this challenge and enable technical solutions and social institutions in a mutually reinforcing dynamic to achieve Responsible AI goals. Our approach is novel in its sociotechnical approach and its aim in tackling the structural issues that cannot be solved within the narrow confines of AI technical research. We then explore possible features of the proposed infrastructure and discuss how it may help solve example problems recently studied in the field.

A Decentralized Approach Towards Responsible AI in Social Ecosystems

The environment we are in can affect our mood and behavior. One environment factor is weather, which is linked to sentiment as expressed on social media. However, less is known about how integrating changes in weather, along with time and location contextual cues, can improve sentiment detection and understanding. In this paper, we explore the effects of three contextual features--weather, location, and time--on expressed sentiment in social media. Leveraging a large Snapchat dataset, we provide extensive experimental evidence that including contextual features in addition to textual features significantly improves textual sentiment detection performance by 3\% over transformer-based language models. Our results also generalize cross-domain to Twitter. Ablation studies indicate the relative importance of weather compared to location and time. We also conduct correlation analyses on 8 million Snapchat posts to highlight the link between past weather and current sentiment, showing that weather has a lasting impact on mood.  Users generally exhibit more positive sentiment in better weather conditions as well as in improved weather conditions. Additionally, we show that temperature's link with mood holds after controlling for time or population density, but there exist geographical differences in how temperature affects mood. Our work demonstrates the effectiveness of including external contexts in linguistic tasks and carries design implications for researchers and designers of social media.

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