United States

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.

ICWSM 2022

Limits of Multilayer Diffusion Network Inference in Social Media Research

multilayer networks

network inference

information diffusion

social media

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2021

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).


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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.

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.

This Candle Has No Smell: Detecting the effect of Covid anosmia on Amazon reviews using Bayesian Vector Autoregression

During global health crises, the use of data becomes critical to control the spread of infections, to inform the general public and to foster safe behaviors. The ability of people to read and understand data (i.e., data literacy) has the potential to affect human behaviors. In this paper, we study the ability of non-expert human subjects making accurate interpretations of complex pandemic data visualisations designed for general public consumption. We present them with popular plots and graphs that have been shown by traditional and social media, and ask them to answer questions to assess their data literacy at three levels: extracting information, finding relationships among data, and expanding or predicting information. Our results show the presence of variance in interpretations, and reveal insights into how messages communicated through data may be perceived differently by different people. We also highlight the importance of designing communication strategies that ensure the spread of the right message through data.

Exploring Data Literacy Levels in the Crowd -- The Case of COVID-19

While the contagious nature of online toxicity sparked increasing interest in early detection and prevention of it, most of the previous studies focus on the Western world. In this work, we study toxicity triggers in an online community for discussing issues regarding Singapore and conduct a comparative study of toxicity triggers between Singapore and New York City.

Understanding Toxicity Triggers on Reddit in the Context of Singapore

Bias in news reporting can lead to tribalism and division on important issues. Scalable and reliable measurement of such biases is an important first step in addressing them. In this work, based on the intuition that media bias is captured by the tone and word choices in articles, we propose a framework for modeling the relative bias of media outlets through masked token prediction via large-scale pretrained masked language models fine-tuned on articles form news outlets. Through experiments on five diverse and politically polarized topics we show that our framework can capture media bias towards these topics with high reliability. Additionally, our experiments show that our framework is general, in that language models fine-tuned on one topic can be applied to other topics with little drop in performance.

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