**Introduction:** The well-being of academics at universities, including students, staff, and faculty is a topic of increasing concern. The lack of well-being may be attributed to the current academic context which does not consistently provide cues that affirm social inclusion to all members of the academic population (Estrada et al., 2018). The lack of cues that affirm the dignity of others has consequences and undermines a basic need that people are more likely to survive and experience well-being when feeling socially connected (Baumeister & Leary, 1995; Dunbar & Barrett, 2007). Further, literature shows that social connection can reduce experiences of stress in pressured environments such as academia (Holt-Lunstad et al., 2015; USPHS, 2023; Valtorta et al., 2016). This study examines the role of kindness (defined as actions that affirm dignity and social inclusion) in promoting connectivity and well-being among academics. 
**Methods:** The Social Influence of Kindness Study was conducted through a one-time anonymous 10-minute online survey via Qualtrics. Participants were recruited using the snowball method via email and social media based on interest in taking a 10-minute survey about Kindness and Dignity during Spring 2020. For this study, 182 participants were selected for analysis based on their occupation designation of studying or working within an academic institution. Each survey included a written reflective micro-narrative in response to the prompt "recall an experience of kindness" and consisted of measures of Kindness Given, Kindness Received, Institutional Identity, Stress, and Well-Being. 
**Results:** We found engaging in acts of kindness, as measured by two novel psychometric rating scales Kindness Received (alpha=.927) and Kindness Given (alpha=.859), was significantly associated with increased institutional identity, well-being, and reduced stress. Our structural equation model (SEM) further validated the correlation results with receiving kindness. Interestingly through the SEM we found conducting acts of kindness had a significant negative relationship with institutional identity and reduced stress. Qualitative analysis of micronarratives on kindness showed that feeling safe and being acknowledged are the most common ways people describe experiencing kindness, an affirmation of their dignity. 
**Conclusions and Discussion:** This study shows that receiving kindness among academics has the potential to improve institutional identity, well-being, and reduce stress. Further, the results show that receiving kindness and giving kindness can be reliably measured amongst students and faculty in academia. We provide evidence that reflecting on experiences of kindness promotes positivity, with the potential to encourage people to conduct further acts of kindness. Together these findings provide knowledge that can inform workshops and interventions to improve social connection and inclusion amongst diverse academics. This study contributes to broader research highlighting positive contextual factors that promote the persistence of diverse students and faculty in academia. 
**Acknowledgments:** The project was funded by NIH grant #R01GM138700

The Influence of Kindness on Academic Well-Being - Poster

Achieving gender equality is an important pillar for humankind’s sustainable future. Pioneering data-driven gender bias research is based on large-scale public records such as scientific papers, patents, and company registrations, covering female researchers, inventors and entrepreneurs, and so on. Since gender information is often missing in relevant datasets, studies rely on tools to infer genders from names. However, available open-sourced Chinese gender-guessing tools are not yet suitable for scientific purposes, which may be partially responsible for female Chinese being underrepresented in mainstream gender bias research and affect their universality. Specifically, these tools focus on character-level information while overlooking the fact that the combinations of Chinese characters in multi-character names, as well as the components and pronunciations of characters, convey important messages. As a first effort, we design a Chinese Heterogeneous Graph Attention (CHGAT) model to capture the heterogeneity in component relationships and incorporate the pronunciations of characters. Our model largely surpasses current tools and also outperforms the state-of-the-art algorithm. Last but not least, the most popular Chinese name-gender dataset is single-character based with far less female coverage from an unreliable source, naturally hindering relevant studies. We open-source a more balanced multi-character dataset from an official source together with our code, hoping to help future research promoting gender equality.

For the Underrepresented in Gender Bias Research: Chinese Name Gender Prediction with Heterogeneous Graph Attention Network

In this work, we examine the many approaches that have been developed to measure the fairness of image tagging systems, finding that these cluster into five distinct groups, each with their own analytic foundations. We further identify a diverse range of normative concerns that are often collapsed under the terms "unfairness," "bias," or even "discrimination" when discussing problematic cases of image tagging. In particular, we offer a four-part taxonomy of the harms that might be caused by image tagging systems, offering concrete examples of each. We then consider how existing measurement approaches map to each of these harms. In so doing, we demonstrate that a wide range of measurement approaches can help to get at a wide range of harms.  Our findings also highlight that no one measurement approach will provide a definitive result, nor will it be possible to infer from the chosen approach which harm researchers are actually seeking to measure. Lastly, equipped with a more granular understanding of the harms of image tagging systems, we show that attempts to mitigate some of these harms may be in tension.

Taxonomizing and Measuring Representational Harms: A Look at Image Tagging

Fairness in influence maximization has been a very active research topic recently. Most works in this context study the question of how to find seeding strategies (deterministic or probabilistic) such that nodes or communities in the network get their fair share of coverage. Different fairness criteria have been used in this context. All these works assume that the entity that is spreading the information has an inherent interest in spreading the information fairly, otherwise why would they want to use the developed fair algorithms? This assumption may however be flawed in reality -- the spreading entity may be purely efficiency-oriented. In this paper we propose to study two optimization problems with the goal to modify the network structure by adding links in such a way that efficiency-oriented information spreading becomes automatically fair. We study the proposed optimization problems both from a theoretical and experimental perspective, that is, we give several hardness and hardness of approximation results, provide efficient algorithms for some special cases, and more importantly provide heuristics for solving one of the problems in practice. In our experimental study we then first compare the proposed heuristics against each other and establish the most successful one. In a second experiment, we then show that our approach can be very successful in practice. That is, we show that already after adding a few edges to the networks the greedy algorithm that purely maximizes spread surpasses all fairness-tailored algorithms in terms of ex-post fairness. Maybe surprisingly, we even show that our approach achieves ex-post fairness values that are comparable or even better than the ex-ante fairness values of the currently most efficient algorithms that optimize ex-ante fairness.

Improving Fairness in Information Exposure by Adding Links

Recent years have witnessed increasing concerns towards unfair decisions made by machine learning algorithms. To improve fairness in model decisions, various fairness notions have been proposed and many fairness-aware methods are developed. However, most of existing definitions and methods focus only on single-label classification. Fairness for multi-label classification, where each instance is associated with more than one labels, is still yet to establish. To fill this gap, we study fairness-aware multi-label classification in this paper. We start by extending Demographic Parity (DP) and Equalized Opportunity (EOp), two popular fairness notions, to multi-label classification scenarios. Through a systematic study, we show that on multi-label data, because of unevenly distributed labels, EOp usually fails to construct a reliable estimate on labels with few instances. We then propose a new framework named Similarity s-induced Fairness (sγ -SimFair). This new framework utilizes data that have similar labels when estimating fairness on a particular label group for better stability, and can unify DP and EOp. Theoretical analysis and experimental results on real-world datasets together demonstrate the advantage of sγ -SimFair over existing methods on multi-label classification tasks.

SimFair: A Unified Framework for Fairness-Aware Multi-Label Classification

**Objective** The International Committee of Medical Journal Editors (ICMJE) provided a set of minimum criteria for authorship.1 These recommendations have been adapted for all sciences in an article by McNutt and colleagues.2 The main difference between these 2 sets of recommendations is that the science-wide recommendations do not require authors to draft or revise the work.1,2 This study aimed to identify the proportion of authors who, based on their self-compiled Contributor Role Taxonomy (CRediT) statements, did not meet the minimum criteria for authorship (ie, were honorary authors) according to these 2 sets of recommendations. Furthermore, the study aimed to identify the proportion of authors who supplied only resources and/or funding to a study. Such authors were identified as “supply authors,” and this practice is considered to be a subtype of honorary authorship. 
 **Design** Cross-sectional study of CRediT statements published in scholarly articles. The Public Library of Science (PLOS) provided CRediT statements and associated data for authors of articles published in PLOS journals between July 2017 and October 2021. Two investigators independently evaluated the authorship recommendations1,2 and developed logical operations of CRediT items for each recommendation. A third investigator acted as an arbiter in case of disagreement. The criteria related to approval and accountability for both recommendations could not be verified because of current CRediT items. For the second objective of the study, authors who contributed only to roles funding acquisition and/or resources were identified. 
 **Results** A total of 629,046 CRediT statements (1 per author) originating from 82,683 journal articles were included. Of the CRediT statements, 34.8% (n = 218,563; 95% CI, 34.7%34.9%) indicated that the contributions provided by the author were not sufficient to qualify for authorship according to the ICMJE recommendations. Based on science-wide recommendations, 3.6% (n = 22,575; 95% CI, 3.5%-3.6%) of the authors did not qualify for authorship. Sensitivity analyses accounting for potentially ambiguous CRediT items provided similar results. The odds of fulfilling only 1 of the recommendations steadily decreased from 2017 to 2021 (Table 1), and authors of articles published in nonmedical journals had 1.11 times the odds to fulfill only 1 of the recommendations compared with authors of articles published in medical journals. Overall, 8394 authors (1.33%; 95% CI, 1.31%-1.36%) were “supply authors.” Their prevalence decreased in the years from 2017 to 2019 (difference in proportion, 0.6%; 95% CI, 0.57%-0.63%) but remained unchanged from 2019 to 2021 (0%; 95% CI, −0.02% to 0.02%). 
 **Conclusions** Based on self-compiled CRediT statements, honorary authorship is still prevalent in science, although it seems to have steadily decreased in recent years. A seemingly minor edit applied to the ICMJE recommendations resulted in substantially different authorship requirements. Efforts should be directed toward developing consensus on core tasks to qualify for authorship that are widely applicable in science. 
Additional strategies should be implemented to address 
“supply authorship.” 
 **Table 1. Prevalence of Honorary Authorship Based on CRediT Statements Depending on 2 Authorship Recommendations and 
the Association of Year and Journal Area With Disagreements 
Between Recommendations** 
 ![](https://assets.underline.io/uploads/markdown_image/1/image/e020149a07644589bc36ea5518d9e003.png)
![](https://assets.underline.io/uploads/markdown_image/1/image/b2f5d13c1f1a80d2be85c7493c4f9bd0.png)
**References**
1.	International Committee of Medical Journal Editors (ICMJE). Recommendations for the conduct, reporting, editing, and publication of scholarly work in medical journals. Updated December 2021. Accessed February 27, 2022. http://www.icmje.org/icmje-recommendations.pdf 
2.	McNutt MK, Bradford M, Drazen JM, et al. Transparency in authors’ contributions and responsibilities to promote integrity in scientific publication. Proc Natl Acad Sci U S A. 2018;115(11):2557-2560. doi:10.1073/pnas.1715374115
1College of Veterinary Medicine, Cornell University, Ithaca, 
NY, USA, nicoladiggi@gmail.com; 2Journal of Small Animal 
Practice, British Small Animal Veterinary Association, 
Gloucestershire, UK; 3Department of Oral and Maxillofacial 
Surgery, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, the Netherlands; 4Private practice of orthodontics, Milan, Italy; 5École de bibliothéconomie et des sciences de l’information, Université de Montréal, Montréal, QC, Canada; 6School of Electrical and Computer 
Engineering, Oklahoma State University, Stillwater, OK, USA 
 **Conflict of Interest Disclosures** Nicola Di Girolamo is an editor in chief of 2 peer-reviewed journals, 1 published by Elsevier and 1 by Wiley. No other disclosures were reported. 
 **Additional Information** The protocol of this research study was registered in the Open Science Framework (https://osf.io/ezpxs/).

![](https://assets.underline.io/uploads/markdown_image/1/image/3b7cc05bc88eb604a84a4c5bbd38244a.png)

Prevalence of Honorary Authorship According to Different Authorship Recommendations and Contributor Role Taxonomy (CRediT) Statements | VIDEO

Fact-checking has become increasingly important due to the speed with which both information and misinformation can spread in the modern media ecosystem. Therefore, researchers have been exploring how fact-checking can be automated, using techniques based on natural language processing, machine learning, knowledge representation, and databases to automatically predict the veracity of claims. In this paper, we survey automated fact-checking stemming from natural language processing, and discuss its connections to related tasks and disciplines. In this process, we present an overview of existing datasets and models, aiming to unify the various definitions given and identify common concepts. Finally, we highlight challenges for future research.

A Survey on Automated Fact-Checking

COVID-19 is a new strain of Coronavirus that had not been discovered in humans previously. It is a disease brought about by SARS-CoV-2 that can trigger a respiratory tract infection. The pandemic has affected people’s lives negatively across the U.S. It was suggested that racial/ethnic minorities were mostly affected by the outburst of this pandemic according to data from the survey people took. The pandemic has affected employment according to the finding from the data filled out by the individuals. More people have lost their jobs because it was not considered essential. As a result, the poverty rate went up. This research will be carried out with community partner organizations, who will recruit participants from communities to take a structured survey and serology test. The purpose of this research is to explore the economic impact of COVID-19 in Delaware’s underserved communities. Specifically, this study will investigate unemployment and household income during the COVID-19 pandemic. The study will also examine whether there are differentials in unemployment and household income by race, gender, and educational level. The significance of this research is to understand the economic impact of the COVID-19 pandemic in underserved communities and bring awareness.

Economic impact of COVID-19 in Delware underserved communities.

Accurate user and item embedding learning is crucial for modern recommender systems. However, most existing recommendation techniques have thus far focused on modeling users' preferences over singular type of user-item interactions. Many practical recommendation scenarios involve multi-typed user interactive behaviors (e.g., page view, add-to-favorite and purchase), which presents unique challenges that cannot be handled by current recommendation solutions. In particular: i) complex inter-dependencies across different types of user behaviors; ii) the incorporation of knowledge-aware item relations into the multi-behavior recommendation framework; iii) dynamic characteristics of multi-typed user-item interactions. To tackle these challenges, this work proposes a Knowledge-Enhanced Hierarchical Graph Transformer Network (KHGT), to investigate multi-typed interactive patterns between users and items in recommender systems. Specifically, KHGT is build upon a graph-structured neural architecture to i) capture type-specific behavior semantics; ii) explicitly discriminate which types of user-item interactions are more important in assisting the forecasting task on the target behavior. Additionally, we further integrate the multi-modal graph attention layer with temporal encoding strategy, to empower the learned embeddings be reflective of both dedicated multiplex user-item and item-item collaborative relations, as well as the underlying interaction dynamics. Extensive experiments conducted on three real-world datasets show that KHGT consistently outperforms many state-of-the-art recommendation methods across various evaluation settings. Our implementation is available in https://github.com/akaxlh/KHGT.

Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation

The present study examined whether acquisition of Japanese particles ga and wa is a precondition for Japanese-speaking children to pass False-Belief Task. The two particles ga and wa are different in that the former is attached to new information and the latter marks old information. Mastering appropriate use of these two particles requires the mind reading ability to understand what is known or unknown to others, hence it can be potentially related to Theory of Mind. The result of an online experiment with Japanese-speaking children aged 3 to 7 showed that children who gained higher accuracy for items about the use of wa in Sentence Repetition Task were more likely to pass False-Belief Task. This suggests that acquisition of grammatical elements expressing information status might be a prerequisite to pass False-Belief Tasks as it helps children understand other’s mind.

Underline digital video library

The Influence of Kindness on Academic Well-Being - Poster

For the Underrepresented in Gender Bias Research: Chinese Name Gender Prediction with Heterogeneous Graph Attention Network

Taxonomizing and Measuring Representational Harms: A Look at Image Tagging

Improving Fairness in Information Exposure by Adding Links

SimFair: A Unified Framework for Fairness-Aware Multi-Label Classification

Prevalence of Honorary Authorship According to Different Authorship Recommendations and Contributor Role Taxonomy (CRediT) Statements | VIDEO

A Survey on Automated Fact-Checking

Economic impact of COVID-19 in Delware underserved communities.

Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation

Is Acquisition of Japanese Particles Ga and Wa a Key to Theory of Mind Development? Evidence from Online Experiment with Sentence Repetition Task

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