United States

Modelling differential stress expressions in urban and rural regions in China can provide a better understanding of the effects of urbanization on psychological well-being in a country that has rapidly grown economically in the last two decades. Using Weibo posts from over 65,000 users across 329 counties, we build hierarchical mixed-effects models to understand linguistic differences in the experiences and expressions of stress in urban-rural China. We analyze phrases, topical themes, and psycho-linguistic word choices in Weibo posts mentioning stress to better understand appraisal differences surrounding psychological stress in urban and rural communities in China; we then compare them with large-scale polls from Gallup. After controlling for socioeconomic and gender differences, we find that rural communities tend to express stress in emotional and personal themes such as relationships, health, and opportunity while users in urban areas express stress using relative, temporal, and external themes such as work, politics, and economics. We find corroborative trends in physical, financial, and social wellness with urbanization in Gallup polls.

ICWSM 2022

Understanding Urban-Rural Differences in Stress in China Using Hierarchical Modeling

china

urbanization

hierarchical linear modeling

text mining

stress

[![](https://assets.underline.io/uploads/markdown_image/1/image/81a3c0317d24f663b49b996875024d45.png)](https://aclanthology.org/volumes/2022.coling-1/)



### THE CONFERENCE WE KNOW AND WE WANT.
**COLING**, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes but also in industrial research departments including tech-startups. COLING provides opportunities for all these communities to showcase their exciting discovery.

In fall of 2022, COLING will be held in Gyeongju in a hybrid format. All participants can either present at the venue site or join virtually. As more people get vaccinated, we are happy to provide safer environments for our colleagues. We believe that COLING 2022 will be one of the conferences, free from the pandemic. The hybrid format gives presenters and sponsors a valuable opportunity to promote their companies in both an online and in-person venue. For the first time in a long time, customers can interact with their sponsor's products first-hand. The online venue, too, gives sponsors the chance to network with those unable to attend the in-person session.

The hybrid format ultimately lends itself to greater exposure for our sponsors: COLING2022 will let you reach more potential partners and customers than ever before!

To gain access to this event page you are required to register. Find more information and pay the registration fee on event the organizer’s website **[https://coling2022.org/reg](https://coling2022.org/reg)**

COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

technical paper

**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
**

The International AAAI Conference on Web and Social Media (ICWSM) is a forum for researchers from multiple disciplines to come together to share knowledge, discuss ideas, exchange information, and learn about cutting-edge research in diverse fields with the common theme of online social media. This overall theme includes research in new perspectives in social theories, as well as computational algorithms for analyzing social media. ICWSM is a singularly fitting venue for research that blends social science and computational approaches to answer important and challenging questions about human social behavior through social media while advancing computational tools for vast and unstructured data.


ICWSM, now in its sixteenth year, has become one of the premier venues for computational social science, and previous years of ICWSM have featured papers, posters, and demos that draw upon network science, machine learning, computational linguistics, sociology, communication, and political science. The uniqueness of the venue and the quality of submissions have contributed to a rapidly growing conference, and a competitive acceptance rate of approximately 20% for full-length research papers published in the proceedings by the Association for the Advancement of Artificial Intelligence (AAAI).


**ICWSM Code of Conduct**

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Additionally, participants are encouraged to be courteous when sharing screen captures and photographs of conference events. Seek permission when possible and respect requests to take down images if those featured ask. Concerns around code of conduct or inclusion may be sent to icwsm22@aaai.org. If you have any concerns or items to report, please reach out to the General Chairs: Diyi Yang and Yelena Mejova.

A ticket is required to attend the event, please use the below URL to register for the event if you have not already done so:

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Virtual-Only:
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Registration Is Required

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.

A breadth of literature has examined how gig workers use online forums. The past literature focuses primarily on how gig workers for mainstream corporate platforms leverage forums. Yet, marginalization, stigma, censorship, and criminalization all shape how people, including gig workers, use digital technology. In this work, we seek to take a first step toward understanding how marginalized, stigmatized, digitally censored, and in some cases criminalized gig workers leverage the affordances of online public forums to build community and increase their welfare. To do so, we conduct a qualitative analysis of 4,000 posts and 25,851 comments shared over four months in a large online public forum used by sex workers for peer support. Sex workers sit at the intersection of multiple marginalized communities, and thus offer a lens into the broader use of forums by marginalized labor communities. Our findings offer insight into how these workers use moderation to preserve their safety and community. Further, we highlight similarities and differences between how this community utilizes the forum platform and prior scholarship on the use of online forums by gig workers.

Community Under Surveillance: Impacts of Marginalization on an Online Labor Forum

Evacuations have a significant impact on saving human lives during hurricanes. However, as a complex dynamic process, it is typically difficult to know  individual evacuation decisions in real-time. Since a large amount of information is continuously posted through social media platforms, we can use them to understand individual evacuation behavior. In this paper, we collect tweets during Hurricane Irma in 2017 and train a text classifier in an active learning way to distinguish tweets expressing positive evacuation decisions from both negative and irrelevant ones. Additionally, we perform a demographic analysis and content clustering to investigate the potential causes and correlates of evacuation decisions. The results can be used to help inform planning strategies of emergency response agencies.

Identifying Hurricane Evacuation Intent on Twitter

Information on social media spreads through an underlying diffusion network that connects people of common interests and opinions. This diffusion network often comprises multiple layers, each capturing the spreading dynamics of a certain type of information characterized by, for example, topic, language, or attitude. Researchers have previously proposed methods to infer these underlying multilayer diffusion networks from observed spreading patterns but little is known about how well these methods perform across the range of realistic spreading data. In this paper, we conduct an extensive series of synthetic data experiments to systematically analyze the performance of the multilayer diffusion network inference framework, under varied network structure (e.g. density, number of layers) and information diffusion settings (e.g. cascade size, layer mixing) that are designed to mimic real-world spreading on social media. Our results show extreme performance variation of the inference framework: notably, it achieves much higher accuracy when inferring a denser diffusion network, while it fails to decompose the diffusion network correctly when most cascades in the data reach a limited audience. In demonstrating the conditions under which the inference accuracy is extremely low, our paper highlights the need to carefully evaluate the applicability of the inference before running it on real data. Practically, our results serve as a reference for this evaluation, and our publicly available implementation, which outperforms previous implementations in accuracy, supports further testing under personalized settings.

Limits of Multilayer Diffusion Network Inference in Social Media Research

The Internet Engineering Task Force (IETF) is responsible for producing the standards that underpin the web (e.g., HTTP, WebSockets, and WebRTC). While the IETF follows an open, consensus-driven process, protocol standardisation is inherently social and political, and latent influential structures might exist in the community. Exploring and understanding these is essential to ensuring the IETF's resilience and openness. We use network analysis to explore the social graphs of IETF participants and the influence that key contributors have.
We show that a small core dominates: the top 10% of participants contribute 43.75% of emails and come from a relatively small set of organisations. On the other hand, we also find that influence has become relatively more decentralised with time.
IETF participants also propose and work on protocol drafts that are either adopted by a working group for further refinement or get rejected at the early stage.
Using the social graph features combined with email text features, we perform regression analysis to understand the effect of user influence on the success of proposed protocol drafts being adopted. Our findings shed useful insights into behavior of participants across time, correlation between influence and success in draft adoption, and the significance of affiliated organisations in development of protocol drafts.

The Web We Weave: Untangling the Social Graph of the IETF

Due to the rise in toxic speech on social media and other
online platforms, there is a growing need for systems that
could automatically flag or filter such content. Various supervised
machine learning approaches have been proposed,
trained from manually-annotated toxic speech corpora. However,
annotators sometimes struggle to judge or to agree on
which text is toxic and which group is being targeted in a
given text. This could be due to bias, subjectivity, or unfamiliarity
with used terminology, (e.g. domain language, slang).
In this paper, we propose the use of a knowledge graph to help
in better understanding such toxic speech annotation issues.
Our empirical results show that 3% in a sample of 19k texts
mention terms associated with frequently attacked gender and
sexual orientation groups that were not correctly identified by
the annotators.

Supporting Online Toxicity Detection with Knowledge Graphs

Emojis come with prepacked semantics making them great candidates to create new forms of more accessible communications. Yet, little is known about how much of this emojis semantic is agreed upon by humans, outside of textual contexts. Thus, we collected a crowdsourced dataset of one-word emoji descriptions for 1,289 emojis presented to participants with no surrounding text. The emojis and their interpretations were then examined for ambiguity. We find that with 30 annotations per emoji, 16 emojis (1.2%) are completely unambiguous, whereas 55 emojis (4.3%) are so ambiguous that their descriptions are indistinguishable from randomly chosen descriptions. Most of studied emojis are spread out between the two extremes. Furthermore, investigating the ambiguity of different types of emojis, we find that an important factor is the extent to which an emoji has an embedded symbolical meaning drawn from an established code-book of symbols. We conclude by discussing design implications.

On the Context-Free Amiguity of Emoji: A Data-Driven Study of 1,289 Emojis

We argue that we need to evaluate model interpretability methods {\em in the wild}, in situations where professionals make critical decisions, and models can potentially assist them. We present an {\bf in-the-wild evaluation} of token attribution based on Deep Taylor Decomposition \cite{chefer2021transformer}, {\bf with professional journalists} performing reliability assessments. We find that using this method in conjunction with RoBERTa-Large, fine-tuned on the Gossip Corpus, led to {\bf faster and better human decision-making}, as well as a more critical attitude toward news sources among the journalists. We present a comparison of human and model rationales, as well as a qualitative analysis of the journalists' experiences with machine-in-the-loop decision making.

Evaluating Deep Taylor Decomposition for Reliability Assessment in the Wild

Despite the increasing interest in cyberbullying detection, existing efforts have largely been limited to experiments on a single platform and their generalisability across different social media platforms has received less attention. We propose XP-CB, a novel cross-platform framework based on Transformers and adversarial learning. XP-CB can enhance a Transformer leveraging unlabelled data from the source and target platforms to come up with a common representation while preventing platform-specific training. To validate our proposed framework, we experiment on cyberbullying datasets from three different platforms through six cross-platform configurations, showing its effectiveness with both BERT and RoBERTa as the underlying Transformer models.

Cyberbullying detection across social media platforms via platform-aware adversarial encoding

Because of their stigmatized social status, sexual and gender minority (SGM; e.g., gay, transgender) people experience minority stress (i.e., identity-based stress arising from adverse social conditions). Given that minority stress is the leading framework for understanding health inequity among SGM people, researchers and clinicians need accurate methods to detect minority stress. Since social media fulfills important developmental, affiliative, and coping functions for SGM people, social media may be an ecologically valid channel for detecting minority stress. In this paper, we propose a bidirectional long short-term memory (BI-LSTM) network for classifying minority stress dis-closed on Reddit. Our experiments on a dataset of 12,645 Reddit posts resulted in an average accuracy of 65%.

Classifying Minority Stress Disclosure on Social Media with Bidirectional Long Short-Term Memory

While there have been many efforts to monitor or predict Covid using digital traces such as social media, one of the most distinctive and diagnostically important symptoms of Covid -- anosmia, or loss of smell -- remains elusive due to the infrequency of discussions of smell online. It was recently hypothesized that an inadvertent indicator of this key symptom may be misplaced complaints in Amazon reviews that scented products such as candles have no smell. We develop a novel Bayesian vector autoregression model to test this hypothesis, finding that "no smell" reviews do indeed reflect changes in US Covid cases even when controlling for the seasonality of those reviews. A series of robustness checks suggests that this effect is also seen in perfume reviews, and did not hold for the flu prior to Covid. These results suggests that hypothesis-driven models of inadvertent traces may be useful for tracking epidemics using digital records.

Understanding Urban-Rural Differences in Stress in China Using Hierarchical Modeling

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Understanding Urban-Rural Differences in Stress in China Using Hierarchical Modeling

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