bluuebunny/crossref_metadata_2025 huggingface dataset:
A conformally invariant Yang–Mills type energy and equation on 6-manifolds
Authors: Gover A. Rod , Peterson Lawrence J. , Sleigh Callum
Published: November 2022

We define a conformally invariant action [Formula: see text] on gauge connections on a closed pseudo-Riemannian manifold [Formula: see text] of dimension 6. At leading order this is quadratic in the gauge connection. The Euler–Lagrange equations of [Formula: see text], with respect to variation of the gauge connection, provide a higher-order conformally invariant analogue of the (source-free) Yang–Mills equations. For any gauge connection [Formula: see text] on [Formula: see text], we define [Formula: see text] by first defining a Lagrangian density associated to [Formula: see text]. This is not conformally invariant but has a conformal transformation analogous to a [Formula: see text]-curvature. Integrating this density provides the conformally invariant action.

In the special case that we apply [Formula: see text] to the conformal Cartan-tractor connection, the functional gradient recovers the natural conformal curvature invariant called the Fefferman–Graham obstruction tensor. So in this case, the Euler–Lagrange equations are exactly the “obstruction-flat” condition for 6-manifolds. This extends known results for 4-dimensional pseudo-Riemannian manifolds where the Bach tensor is recovered in the Yang–Mills equations of the Cartan-tractor connection.
On spined product decompositions of completely regular semigroups
Authors: Debnath R. , Bhuniya A. K.
Published: November 2022

Here, we have considered two equivalence relations [Formula: see text] and [Formula: see text] that are congruences on regular orthogroups but not on completely regular semigroups, in general. The congruence openings [Formula: see text] and [Formula: see text] of [Formula: see text] and [Formula: see text], respectively, induce new subvarieties [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] for every [Formula: see text]; and are such that [Formula: see text] and [Formula: see text]. The decompositions of an arbitrary completely regular semigroup which are presented here generalize several spined product decompositions for particular classes of completely regular semigroups already given by several authors [M. Petrich and N. R. Reilly, Completely Regular Semigroup (Wiley, New York, 1999)], and thus contributes to a unification of the theory.
A Double Metan-Semantic Search Model Based on Ontology and Semantic Similarity: Asthma Disease
Authors: Belabed Mourad , Dennai Abdeslem
Published: November 2022

With the exponential and rapid growth of online resources in recent years, there has been a huge increase in the use of search engines; these are also one of the most common ways to navigate the Web content without taking into account, in general, the request meaning by which was successfully added the user’s webpage provides us with a lot of results. This problem has led to the integration of semantics in the search for information on the Web (Semantic Web). The use of semantic tools, such as ontology, WordNet dictionary, semantic similarity measure, etc., has contributed to the semantic search development and more particularly, semantic Metan-search. The success of semantic search is closely linked to the availability of domain ontologies. The objective of this paper is to propose a double model of repetitive semantic search, called Double Metan-Semantic Search Model (2[Formula: see text]-SSM). On the one hand, it is assisted and based on the concepts extracted from the user’s search domain ontology, which will permit the user to choose a concept from this list of concepts and launch their search; on the other hand, it is free, in that the user enters their own concept and launches their search. This is based on WordNet tool, user’s same search domain ontology and the semantic similarity calculation techniques between concepts in the same ontology. The result of this model is a set of URL links. The term Metan indicates that the search is done in depth ([Formula: see text]-SS) via choosing each time a URL result by the user. Its experimentation in the asthma disease field gave very promising results in quantity and quality of information via the URL link results (semantic support).
The Structural, Morphological, and Resistive Switching Memory Studies of Zinc Tungstate Prepared by Hydrothermal Method
Authors: Gowtham M. , Sivakumar Chandrasekar , Mohanbabu B. , Chandrasekar Narendhar , Mohanraj K. , Balraj Babu , Ho Mon-Shu , Senthil Kumar N.
Published: November 2022

Nanostructure-based resistive switching memory devices are being developed for low power, multilevel storage capability, extended retention capacity, and scalable devices. The zinc tungstate (ZnWO[Formula: see text] nanoparticle was prepared via the facile hydrothermal method. The X-ray diffraction technique confirmed ZnWO4 monoclinic phase and crystallite nature. The scanning electron microscope was used to identify the rod-like ZnWO4 nanostructure, and further, the lattice orientation was investigated by high-resolution transmission electron microscopy. X-ray photoelectron spectroscopy confirmed the binding states with composition of ZnWO4. Resistive switching memory based on ZnWO4 was produced and resulted in low-operating voltage.
On the Predictive Value of the (Shadow) Real Interest Rate for the Realized Volatility of Gold-Price Returns
Authors: Pierdzioch Christian , Rohloff Sebastian , Von Campe Roland
Published: November 2022

We use a quasi-out-of-sample forecasting experiment to study the predictive value of a short-term real interest rate for the volatility of gold-price returns. To this end, we use monthly U.S. data for the sample period from 1990/1 to 2022/2, and we study a standard effective-federal-funds-based real interest rate as well as a shadow real interest rate, which accounts for the recent extended zero-lower-bound period. We find that the real interest rate has predictive value for the subsequent realized volatility, and this predictive value turns out to be stronger in several specifications of our forecasting experiment for the shadow real interest rate than for the standard real interest rate. We evaluate the predictive value of forecasts in terms of an asymmetric loss function. Because gold is considered as a safe-haven asset, our results provide some important implications for portfolio decisions of investors.
INVESTMENT BASED ON SIZE, VALUE, MOMENTUM AND INCOME MEASURES: A STUDY IN THE TAIWAN STOCK MARKET
Authors: LU RICHARD , WANG JAI-JEN , WONG WING-KEUNG
Published: November 2022

Cross-sectional characteristics of stocks such as market value, market-to-book ratio and accumulated past return can be applied to formulate equity portfolios in the stock-picking process to generate good profits in some markets, which relate to the well-known size, value and momentum or contrarian strategies in the literature. Alternatively, income measures in financial statements drive investors in stock markets to buy or sell in an intuitive way that can be also used in the stock-picking process to generate good profits in some markets. This study applies these types of information in the formation period to formulate long-short strategies and investigates both the returns and risk profiles in the holding period afterward and checks whether the measures can be used to generate good profits in the Taiwan markets for the period from January 1980 to June 2020. Given different lengths of the holding period and different equity segments, our empirical analysis shows that strategies filtered by the income measure of gross profitability outperform the counterparts filtered by the operating profitability. Moreover, while the momentum or contrarian effect is not, the size and value effects are helpful to improve the performance of long-short strategies filtered by gross profitability.
Comparative Study on the Teaching of Japanese Listening and Speaking in the Language Room of Teachers' Intelligent Multimedia Systems in Chinese and Foreign Universities
Authors: Yu Hao
Published: November 2022

With the popularization of computers and various mobile intelligent terminals, intelligent multimedia teaching systems with learners as the main body are more and more favored by learners, which affects and changes the methods and modes of Japanese teaching to a large extent. Native language teachers are considered the authority of the language. Fluent oral expression and profound knowledge of language and culture make them regarded as ideal foreign language teachers, especially in oral teaching, they are regarded as having incomparable advantages. This research aims to explore the differences between Chinese and foreign teachers in listening and speaking teaching under the teaching conditions of the intelligent multimedia language room, and to try to analyze the reasons for the differences. This paper interviewed 180 sophomores of Japanese majors, 4 Chinese Japanese teachers and 2 Japanese foreign teachers at foreign language universities through a questionnaire survey, and analyzed the statistical data through SPSS 21.0. The 180 students were divided into groups of 30, with 4 groups from non-Japanese teachers' classes and 2 from Japanese teachers' classes. For a long time, they have used the same Japanese listening and speaking textbooks in the intelligent multimedia language room. In order to better improve the quality of teaching, Chinese and foreign teachers should strengthen teaching cooperation and exchanges, rationally improve teaching content, optimize the use of teaching software, and achieve win-win cooperation. In the NJST class, 29.2% of students believe that they spend more time practicing listening in class than speaking. In the NNJST class, close to 39.1% of students believe that teachers spend more time on listening. Fully consider the needs of learners in learning a second language other than Chinese, and through research on Japanese learning strategies and teaching theories, to design and develop an intelligent Japanese teaching system with comprehensive functions, strong applicability, and support for individualized, autonomous and collaborative learning.
A Social-Ecological Approach to Modeling Sense of Virtual Community (SOVC) in Livestreaming Communities
Authors: Kairam Sanjay R. , Mercado Melissa C. , Sumner Steven A.
Published: November 2022

Participation in communities is essential to individual mental and physical health and can yield further benefits for members. With a growing amount of time spent participating in virtual communities, it's increasingly important that we understand how the community experience manifests in and varies across these online spaces. In this paper, we investigate Sense of Virtual Community (SOVC) in the context of live-streaming communities. Through a survey of 1,944 Twitch viewers, we identify that community experiences on Twitch vary along two primary dimensions: belonging, a feeling of membership and support within the group, and cohesion, a feeling that the group is a well-run collective with standards for behavior. Leveraging the Social-Ecological Model, we map behavioral trace data from usage logs to various levels of the social ecology surrounding an individual user's participation within a community, in order to identify which of these can be associated with lower or higher SOVC. We find that features describing activity at the individual and community levels, but not features describing the community member's dyadic relationships, aid in predicting the SOVC that community members feel within channels. We consider implications for the design of live-streaming communities and for fostering the well-being of their members, and we consider theoretical implications for the study of SOVC in modern, interactive online contexts, particularly those fostering large-scale or pseudonymized interactions. We also explore how the Social-Ecological Model can be leveraged in other contexts relevant to Computer-Supported Cooperative Work (CSCW), with implications for future work.
"Cyber security is a dark art": The CISO as Soothsayer
Authors: Da Silva Joseph , Jensen Rikke Bjerg
Published: November 2022

Commercial organisations continue to face a growing and evolving threat of data breaches and system compromises, making their cyber-security function critically important. Many organisations employ a Chief Information Security Officer (CISO) to lead such a function. We conducted in-depth, semi-structured interviews with 15 CISOs and six senior organisational leaders, between October 2019 and July 2020, as part of a wider exploration into the purpose of CISOs and cyber-security functions. In this paper, we employ broader security scholarship related to ontological security and sociological notions of identity work to provide an interpretative analysis of the CISO role in organisations. Research findings reveal that cyber security is an expert system that positions the CISO as an interpreter of something that is mystical, unknown and fearful to the uninitiated. They show how the fearful nature of cyber security contributes to it being considered an ontological threat by the organisation, while responding to that threat contributes to the organisation's overall identity. We further show how cyber security is analogous to a belief system and how one of the roles of the CISO is akin to that of a modern-day soothsayer for senior management; that this role is precarious and, at the same time, superior, leading to alienation within the organisation. Our study also highlights that the CISO identity of protector-from-threat, linked to the precarious position, motivates self-serving actions that we term 'cyber sophistry'. We conclude by outlining a series of implications for both organisations and CISOs.
Venice Was Flooding ... One Tweet at a Time
Authors: Lorini Valerio , Rufolo Paola , Castillo Carlos
Published: November 2022

Before urban flooding actually happens, weather forecasts with varying degrees of precision are available to emergency managers. In the aftermath of the event, authoritative information including Earth Observation (EO) data can be used to estimate precisely the flood extent, possibly after several hours. This study aims to determine how social media information can reduce the inherent uncertainty of the information in the immediate aftermath of an urban flood event. Specifically, the study investigates how to collect relevant social media images and to interpolate such data in order to create a map.

The premise of the study is that social media platforms, when combined with digital surface models, can provide control points for creating a reliable near real-time estimate of the flood extent. In the study, we compared a flood extent map derived from social media with that derived from authoritative altimetry data during one of the worst floods to hit Venice, which occurred in November 2019.

The results of the experiments show a good overall accuracy using several digital surface models. Given the global coverage of such models and the low resources required, we think the methodology proposed could be beneficial for emergency managers. Specifically, we describe how a flood extent map can be made available within 24 h, or even less, after urban flooding strikes a densely inhabited area, where data generated by the public are available.
Exploring the Utility of Social Content for Understanding Future In-Demand Skills
Authors: Mahdavimoghaddam Jalehsadat , Bahuguna Ayush , Bagheri Ebrahim
Published: November 2022

Rapid technological innovations, especially in the information technology space, demand the workforce to be vigilant by acquiring new skills to remain relevant and employable. The workforce needs to be engaged in a continuous lifelong learning process by educating themselves about skills that will be in demand in the future. To do so, it is essential for students, job seekers, and even recruiters to know which skills will be in demand in the future and to invest time and resources in developing these skills. On this basis, the main objective of this paper is to investigate whether social content can offer insight into potential future in-demand skills in the IT job market. Based on the analysis of social content from Reddit and job posting data from Dice and Monster websites, we find that social content related to job skills is a strong indicator for future in-demand skills. We further find that specific social content associated with recruitment-related topics are stronger indicators of future skills. Our findings encourage learners and job seekers to pay close attention to online social content to strategically plan new skills and maximize their employability.
"That Robot Played with Us!" Children's Perceptions of a Robot after a Child-Robot Group Interaction
Authors: Escobar-Planas Marina , Charisi Vicky , Gomez Emilia
Published: November 2022

The design of child-centred, intelligent and collaborative robots is a challenging endeavour, which requires to understand how the implemented robot behaviours and collaboration paradigms affect children's perception about the robot. This paper presents the results of a set of semi-structured interviews of N=81, 5 to 8 years old children who previously interacted in pairs with a robot in the context of a problem-solving task. We manipulated two different factors of the robot behaviour: cognitive reliability in logic game movements (optimal vs sub-optimal) and expressivity in the communication (expressive vs neutral) and we assigned the children in one of the four conditions. At post-intervention interviews, we examined children's perceptions on the robot's attributions, collaboration and social role. Results indicate that a robot's cognitive reliability shapes the helping relationship between the children and the robot, while the robot's expressivity impacts children perception of the robot supportive ability and friendship. Finally, results also indicate that, even if children interact in pairs with the robot, their perceptions about it remain individual, although a good collective task-performance seems to empower children perception of the robot in terms of friendship and reliability.
"We're so much more than the in-game clan": Gaming Experiences and Group Management in Multi-Space Online Communities
Authors: Toombs Austin L. , Lee Ahreum , Guo Zhuang , Buls Jared , Westbrook Abbee , Carr Ian , Wu Yuqing , LaPeter Michael
Published: November 2022

The platforms that host online gaming groups and communities continue to evolve, and it has become possible to join, participate in, and consume content from groups that exist across multiple tools, platforms, and spaces at the same time. In this paper, we explore how groups use and rely upon assemblages of multiple online spaces to accomplish the "work" of participating in these gaming groups. We present an interview study with users of the100.io, a platform that hosts gaming community spaces, helps players find groups, and operates as a gaming event scheduling tool for its users. Contrary to our initial assumptions, we found that users relied upon the100 as a kind of glue for flexibly-interconnected, multi-space group configurations. These multi-space groups support our participants' desires to approach online gaming as a social practice, provide additional accountability among players, and enable multiple forms of social participation within those communities. Our findings point towards opportunities to expand social computing scholarship to better describe how users of online communities flexibly bridge across technical infrastructure.
There is No App for That: Manifestations of the Digital Divides During COVID-19 School Closures in India
Authors: Dhaygude Mrunal Sanjay , Lapsiya Naitik D. , Chakraborty Dipanjan
Published: November 2022

The COVID-19 induced lockdowns forced people to shift several activities, including education, online. However, in the context of online schooling, the Digital Divides have magnified and perpetuated existing inequities in the education system and in society. Through a qualitative study with 48 participants across four stakeholder groups we find that students in under-funded government schools in India largely have not been able to access online classes because of a lack of devices, poor quality of Internet access, unreliable data networks and expensive data plans. We also document attempts by teachers and non-profit workers to use mass media broadcast technologies to work around the issue of digital access, highlighting the importance of a human infrastructure to build resilience during a disruptive event. Socioeconomic factors have also forced several students to drop out of schools and into taking up jobs to support their families. We document the importance of enabling environments and economic safety nets at home for online education to succeed. We present some focus points for researchers and policy makers working in the space of digital divide and education to build more resilient systems through Digital Welfarism.
"Help! Can You Hear Me?": Understanding How Help-Seeking Posts are Overwhelmed on Social Media during a Natural Disaster
Authors: He Changyang , Deng Yue , Yang Wenjie , Li Bo
Published: November 2022

Posting help-seeking requests on social media has been broadly adopted by victims during natural disasters to look for urgent rescue and supplies. The help-seeking requests need to get sufficient public attention and be promptly routed to the intended target(s) for timely responses. However, the huge volume and diverse types of crisis-related posts on social media might limit help-seeking requests to receive adequate engagement and lead to their overwhelm. To understand this problem, this work proposes a mixed-methods approach to figure out the overwhelm situation of help-seeking requests, and individuals' and online communities' strategies to cope. We focused on the 2021 Henan Floods in China and collected 141,674 help-seeking posts with the keyword "Henan Rainstorm Mutual Aid" on a popular Chinese social media platform Weibo. The findings indicate that help-seeking posts confront critical challenges of both external overwhelm (i.e., an enormous number of non-help-seeking posts with the help-seeking-related keyword distracting public attention) and internal overwhelm (i.e., attention inequality with 5% help-seeking posts receiving more than 95% likes, comments, and shares). We discover linguistic and non-linguistic help-seeking strategies that could help to prevent the overwhelm, such as including contact information, disclosing situational vulnerabilities, using subjective narratives, and structuring help-seeking posts to a normalized syntax. We also illustrate how community members spontaneously work to prevent the overwhelm with their collective wisdom (e.g., norm development through discussion) and collaborative work (e.g., cross-community support). We reflect on how the findings enrich the literature in crisis informatics and raise design implications that facilitate effective help-seeking on social media during natural disasters.
"I'm not sure what difference is between their content and mine, other than the person itself"
Authors: Ma Renkai , Kou Yubo
Published: November 2022

How social media platforms could fairly conduct content moderation is gaining attention from society at large. Researchers from HCI and CSCW have investigated whether certain factors could affect how users perceive moderation decisions as fair or unfair. However, little attention has been paid to unpacking or elaborating on the formation processes of users' perceived (un)fairness from their moderation experiences, especially users who monetize their content. By interviewing 21 for-profit YouTubers (i.e., video content creators), we found three primary ways through which participants assess moderation fairness, including equality across their peers, consistency across moderation decisions and policies, and their voice in algorithmic visibility decision-making processes. Building upon the findings, we discuss how our participants' fairness perceptions demonstrate a multi-dimensional notion of moderation fairness and how YouTube implements an algorithmic assemblage to moderate YouTubers. We derive translatable design considerations for a fairer moderation system on platforms affording creator monetization.
Automated Detection of Doxing on Twitter
Authors: Karimi Younes , Squicciarini Anna , Wilson Shomir
Published: November 2022

Doxing refers to the practice of disclosing sensitive personal information about a person without their consent. This form of cyberbullying is an unpleasant and sometimes dangerous phenomenon for online social networks. Although prior work exists on automated identification of other types of cyberbullying, a need exists for methods capable of detecting doxing on Twitter specifically. We propose and evaluate a set of approaches for automatically detecting second- and third-party disclosures on Twitter of sensitive private information, a subset of which constitutes doxing. We summarize our findings of common intentions behind doxing episodes and compare nine different approaches for automated detection based on string-matching and one-hot encoded heuristics, as well as word and contextualized string embedding representations of tweets. We identify an approach providing 96.86% accuracy and 97.37% recall using contextualized string embeddings and conclude by discussing the practicality of our proposed methods.
Counting to be Counted: Anganwadi Workers and Digital Infrastructures of Ambivalent Care
Authors: S P Azhagu Meena , Vaghela Palashi , Pal Joyojeet
Published: November 2022

Data collection on the population is a key mode of public health management in the Global South. This information is seen as a means to improve health metrics through welfare programs. In this study, we examine the changes brought about by an ICT-based Real-Time Monitoring System to the infrastructure of a welfare program and the nature of work of Anganwadi workers in India. Anganwadi workers, traditionally serving as daycare providers and community health workers, are increasingly being asked to serve primarily as data collectors for the new digital system. We ask the question 'cui bono?' to this system by drawing attention to the precarity of Anganwadi workers whose care-work is standardized through this app for 'efficient' monitoring by the Indian state but remains contingent on their relationship with the local community and ability to mobilize resources on the ground. Using auto-ethnographic and interview methods, we find that Anganwadi workers are caught between conflicting demands of state bureaucracy and the situated nature of their care work resulting in forms of ambivalent care. We find that the real-time monitoring apps intended to collect data for efficient delivery of state services end up serving the state's need for performing care through data rhetorics produced at the expense of the professional and personal well-being of the workers, and arguably the communities they serve.
Managing the Transition to Online Freelance Platforms: Self-Directed Socialization
Authors: Blaising Allie , Dabbish Laura
Published: November 2022

Individuals increasingly turn to online freelance platforms to find flexible, remote, knowledge-based work. Yet we have only begun to understand the challenges new freelancers face with limited formal resources to orient themselves to this career. To inform design and policy to support new freelancers, we conducted a qualitative interview study on transitions to online freelance platforms with 27 freelancers working on the Upwork and Fiverr platforms. We found that new freelancers engage in self-directed socialization as they proactively seek support not directly provided by the platform or their clients. This support helps them resolve professional identity ambiguity, learn platform work self-management, build credibility, and overcome self-doubt in their new careers. Our findings suggest opportunities to support new forms of socialization and mentorship to address disparities in access to networks, resources, and knowledge needed to successfully begin an online freelance career.
Reliance and Automation for Human-AI Collaborative Data Labeling Conflict Resolution
Authors: Brachman Michelle , Ashktorab Zahra , Desmond Michael , Duesterwald Evelyn , Dugan Casey , Joshi Narendra Nath , Pan Qian , Sharma Aabhas
Published: November 2022

Human data labeling with multiple labelers and the resulting conflict resolution remains the norm for many enterprise machine learning pipelines. Conflict resolution can be a time-intensive and costly process. Our goal was to study how human-AI collaboration can improve conflict resolution, by enabling users to automate groups of conflict resolution tasks. However, little is known about whether and how people will rely on automation during conflict resolution. Currently, automation commonly uses labelers' majority vote labels for conflict resolution, as the top chosen label by most labelers is often correct. We envisioned a system where an AI would assist in finding cases where the labeler majority vote was wrong and where automation is supported for batches or groups of conflicts. In order to understand whether humans could use labeler and AI information effectively, we investigated how and when users rely on labeler and AI information and on automated group conflict resolution. We ran a study with 144 Mechanical Turk workers. We found that automation increased users' accuracy/time, use of automated conflict resolution was relatively similar regardless of whether the automation was based on labeler or AI selected labels, and providing labeler and AI selected labels may reduce inappropriate reliance on automation.
The Use of Negative Interface Cues to Change Perceptions of Online Retributive Harassment
Authors: Lee Song Mi , Thomer Andrea K. , Lampe Cliff
Published: November 2022

Online retributive harassment refers to a range of abusive online behaviors targeted at offenders with the intent of sanctioning norm violations. Online retributive harassment is common in online interactions, can be devastating in its effect, and is hard to moderate. We examined the role that negative interface cues (i.e., Dislikes, flags) might have in changing people's harassment perceptions to mitigate this activity. We conducted a 4x2 between-subjects experiment (N = 242) to test the effects of interface cues (control with Likes only vs. Dislikes outnumbering Likes vs. Likes outnumbering Dislikes vs. a flag with Likes) and harassment severity (low vs. high) on how people perceive retributive harassment. We hypothesized that Dislikes and flags, despite the presence of Likes, signal social disapproval and descriptive norms against harassment and thus reduce bystanders' belief that retributive harassment is appropriate, deserved, or justified. We found that Dislikes can be effective when they outnumber Likes in high-severity harassment but backfire when outnumbered by Likes. A flag, contrary to popular practice, does not have a significant mitigating effect on perceptions of retributive harassment. We demonstrate the potential of negative interface cues to signal anti-harassment norms to bystanders and discuss alternatives to social media platforms' one-size-fits-all content moderation approach.
Data Protection at a Discount: Investigating the UX of Data Protection from User, Designer, and Business Leader Perspectives
Authors: Chalhoub George , Flechais Ivan
Published: November 2022

Smart homes are dangerous - a sentiment arising from prior research exploring the user experience (UX) of data protection for smart home devices. While this research has explored data protection shortcomings for users, UX is a designed encounter reconciling development, economic, compliance and strategic business priorities. And so, in addition to studying user perspectives, there is a gap in understanding how designers and business leaders influence the UX of data protection. To address this gap, we study smart home users, designers and business leaders, exploring how they experience data protection interactions, regulation, and processes. Our findings confirm that users have poor data protection interactions (e.g., consent and data access requests). We also find that business leaders and designers experience difficulties in identifying, applying, and tailoring suitable processes and practices for data protection for which some have developed "discount data protection": shortcuts, heuristics, and common sense practices to overcome these challenges.
More Gamification Is Not Always Better
Authors: Hadi Mogavi Reza , Haq Ehsan-Ul , Gujar Sujit , Hui Pan , Ma Xiaojuan
Published: November 2022

Community Question Answering Websites (CQAs) like Stack Overflow rely on continuous user contributions to keep their services active. Nevertheless, they often undergo a sharp decline in their user participation during the holiday season, undermining their performance. To address this issue, some CQAs have developed their own special promotional gamification schemes to incentivize users to maintain their contributions throughout the holiday season. These promotional gamification schemes are often time-limited, optional, and run alongside the default gamification schemes of their websites. However, the impact of such promotional gamification schemes on user behavior remains largely unexplored in the existing literature. This paper takes the first steps toward filling this knowledge gap by conducting a large-scale empirical study of a particular promotional gamification scheme called Winter Bash (WB) on the CQA of Stack Overflow. According to our findings, promotional gamification schemes may not be the panacea they are portrayed to be. For example, in the case of WB, we find that the scheme is not effective for improving the collective engagement of all users. Only some particular user types (i.e., experienced and reputable users) are often provoked under WB. Most novice users, who comprise the majority of Stack Overflow website's user base, seem to be indifferent to such a gamification scheme. Our research also shows the importance of studying the quantity and quality of user engagement in unison to better understand the effectiveness of a gamification scheme. Previous gamification studies in the literature have focused predominantly on studying the quantity of user engagement alone. Last but not least, we conclude our paper by presenting some practical considerations for improving the design of future promotional gamification schemes in CQAs and similar platforms.
Understanding Cultural Influence on Perspectives Around Contact Tracing Strategies
Authors: Lu Xi , Jo Eunkyung , Park Seora , Hong Hwajung , Chen Yunan , Epstein Daniel A.
Published: November 2022

Contact tracing, a major way to curb COVID-19 and other epidemics, has been employed worldwide, with human interviewing and proximity tracing technology as two major approaches. While previous research has contributed some understanding of people's perspectives on contact tracing technology, much of this is based in single countries or regions where technology has been deployed. To understand how culture influences people's perceptions toward human tracing and digital tracing, we replicated a mixed-methods survey study conducted in the U.S. in South Korea and compared participants' perspectives. South Korean participants preferred digital tracing to human tracing, contrasting with the U.S. context where no strong preference was observed. We discuss how observed differences in perspective align and contrast with the country's typical cultural dimensions, such as high power distance, informing the perspective that human tracing will have greater accuracy. We emphasize the need for culturally designing contact tracing technology to highlight personal benefits regardless of cultural dimensions, and leverage technology to support social interaction in human tracing.
Make Reddit Great Again: Assessing Community Effects of Moderation Interventions on r/The_Donald
Authors: Trujillo Amaury , Cresci Stefano
Published: November 2022

The subreddit r/The_Donald was repeatedly denounced as a toxic and misbehaving online community, reasons for which it faced a sequence of moderation interventions by Reddit administrators. It was quarantined in June 2019, restricted in February 2020, and finally banned in June 2020, but despite precursory work on the matter, the effects of this sequence of interventions are still unclear. In this work, we follow a multidimensional causal inference approach, with data containing more than 15M posts made in a time frame of 2 years, to examine the effects of such interventions inside and outside of the subreddit. We find that the interventions greatly reduced the activity of problematic users. However, the interventions also caused an increase in toxicity and led users to share more polarized and less factual news. In addition, the restriction had stronger effects than the quarantine, and core users of r/The_Donald suffered stronger effects than the rest of users. Overall, our results provide evidence that the interventions had mixed effects and paint a nuanced picture of the consequences of community-level moderation strategies. We conclude by reflecting on the challenges of policing online platforms and on the implications for the design and deployment of moderation interventions.
Bifurcating Cognitive Attention from Visual Concentration: Utilizing Cooperative Audiovisual Sensing for Demarcating Inattentive Online Meeting Participants
Authors: Kar Pragma , Chattopadhyay Samiran , Chakraborty Sandip
Published: November 2022

The profuse popularity of video conferencing has led to a simultaneous rise in the opportunity for the participants to multitask. Productive multitasking, such as taking notes, browsing for relevant information, etc., can help promote the cognitive attentiveness of participants. However, existing approaches of tagging inattentive participants solely based on their visual concentration on the meeting app fail to work in such instances. This paper proposes EmotiConf -- a novel real-time framework to monitor participants' attentiveness and a non-real-time framework for visual multitask detection without explicitly relying on their visual concentration. EmotiConf utilizes an unconventional observation where the emotional states of attentive participants, captured through their facial expressions, correlate and also correspond to the vocal expression of the speaker and the intent of the speech. Accordingly, EmotiConf develops a software wrapper to tag the inattentive participants while also characterizing visual multitasking instances performed by them. A thorough evaluation of EmotiConf confirms its usability with a high score of >80.
Anticipated versus Actual Effects of Platform Design Change: A Case Study of Twitter's Character Limit
Authors: Gligoric Kristina , Czestochowska Justyna , Anderson Ashton , West Robert
Published: November 2022

The design of online platforms is both critically important and challenging, as any changes may lead to unintended consequences, and it can be hard to predict how users will react. Here we conduct a case study of a particularly important real-world platform design change: Twitter's decision to double the character limit from 140 to 280 characters to soothe users' need to "cram" or "squeeze" their tweets, informed by modeling of historical user behavior. In our analysis, we contrast Twitter's anticipated pre-intervention predictions about user behavior with actual post-intervention user behavior: Did the platform design change lead to the intended user behavior shifts, or did a gap between anticipated and actual behavior emerge? Did different user groups react differently? We find that even though users do not "cram" as much under 280 characters as they used to under 140 characters, emergent "cramming" at the new limit seems to not have been taken into account when designing the platform change. Furthermore, investigating textual features, we find that, although post-intervention "crammed" tweets are longer, their syntactic and semantic characteristics remain similar and indicative of "squeezing". Applying the same approach as Twitter policy-makers, we create updated counterfactual estimates and find that the character limit would need to be increased further to reduce cramming that re-emerged at the new limit. We contribute to the rich literature studying online user behavior with an empirical study that reveals a dynamic interaction between platform design and user behavior, with immediate policy and practical implications for the design of socio-technical systems.
Understanding Questions that Arise When Working with Business Documents
Authors: Jahanbakhsh Farnaz , Nouri Elnaz , Sim Robert , White Ryen W. , Fourney Adam
Published: November 2022

While digital assistants are increasingly used to help with various productivity tasks, less attention has been paid to employing them in the domain of business documents. To build an agent that can handle users' information needs in this domain, we must first understand the types of assistance that users desire when working on their documents. In this work, we present results from two user studies that characterize the information needs and queries of authors, reviewers, and readers of business documents. In the first study, we used experience sampling to collect users' questions in-situ as they were working with their documents, and in the second, we built a human-in-the-loop document Q&A system which rendered assistance with a variety of users' questions. Our results have implications for the design of document assistants that complement AI with human intelligence including whether particular skillsets or roles within the document are needed from human respondents, as well as the challenges around such systems.
Users' Expectations, Experiences, and Concerns With COVID Alert, an Exposure-Notification App
Authors: Huang Yue , Obada-Obieh Borke , Lokam Satya , Beznosov Konstantin (Kosta)
Published: November 2022

We conducted semi-structured interviews with 20 users of Canada's exposure-notification app, COVID Alert. We identified several types of users' mental models for the app. Participants' concerns were found to correlate with their level of understanding of the app. Compared to a centralized contact-tracing app, COVID Alert was favored for its more efficient notification delivery method, its higher privacy protection, and its optional level of cooperation. Based on our findings, we suggest decision-makers rethink the app's privacy-utility trade-off and improve its utility by giving users more control over their data. We also suggest technology companies build and maintain trust with the public. Further, we recommend increasing diagnosed users' motivation to notify the app and encouraging exposed users to follow the guidelines. Last, we provide design suggestions to help users with Unsound and Innocent mental models to better understand the app.
Bargaining with the Black-Box
Authors: Calacci Dan , Pentland Alex
Published: November 2022

The increasing prevalence of large-scale labor aggregation platforms, worker analytics, and algorithmic decision-making by management raises the question of whether workers can use similar technologies to advocate for their own goals. Yet, there are inherent challenges in building worker-centric tools that collect, aggregate, and share data in responsible and ethical ways. In this paper, we present the design and deployment of the Shipt Calculator, a tool developed in collaboration with non-profit worker groups that allows app-based delivery workers to track and share aggregate data about their pay, increasing wage transparency. We first discuss the design challenges inherent to building worker-centric technologies, particularly for informally organized workers, and ground our discussion in the history of worker inquiry and co-research. We then describe some principles from this history and our own lessons in designing the Calculator that can be applied by future researchers and advocates seeking to build technical tools for organizing campaigns. Finally, we share the results of using the Calculator to audit an app's shift to a black-box pay model using data contributed by 140 workers in the Summer of 2020, finding that although the average pay per-order increased under the new payment model, almost half of workers experienced an unannounced pay cut during the shift, and many workers worked shifts that paid under their state's minimum wage. Finally, we discuss how tools like the Calculator demonstrate the important role that aggregate worker data, and a new Digital Workerism, can serve in creating and maintaining a more balanced platform economy.
Towards Sustainable Compressive Population Health: A GAN-based Year-By-Year Imputation Method
Authors: Feng Yujie , Wang Jiangtao , Wang Yasha , Chu Xu
Published: November 2022

Population health monitoring is a fundamental component of the public health system. Due to the high-cost nature of traditional population-wise health-data collection methods, a class of sparse-sampling-completion algorithms are proposed to exploit the spatio-temporal correlation buried under the observed examples. However, for the population health data, a huge challenge for the state-of-the-art completion methods is the unstationary environment. Specifically, the underlying temporal correlation of the population health data are evolving from year to year. To this end, we propose a GAN-based year-by-year completion framework:
uncertainty-aware augmented generative adversarial imputation nets (UAA-GAIN)
, to address the problem of unstationary environment. To further restrain the error accumulation, we develop a stronger generator as well as a stronger discriminator in the min-max equilibrium. A by-product of the augmented GAIN model allows weighting the difficulty of examples. Inspired by the idea of curriculum learning, a better training schedule is implemented in the proposed framework. We evaluate the proposed method on three real-world chronic disease datasets and the results show that UAA-GAIN outperforms other baseline methods in various settings.
Detecting Fake Reviews with Generative Adversarial Networks for Mobile Social Networks
Authors: Qu Zheng , Jia Qingyao , Lyu Chen , Liu Jia , Liu Xiaoying , Zheng Kechen
Published: November 2022

With the growth of mobile social networks (MSNs), crowdsourced information could be used for recommendation to mobile users. However, it is quite vulnerable to Sybil attacks, where attackers post fake information or reviews to mislead users for business benefits. To address this problem, existing detection models mainly use graph-based techniques or extract features of users. However, these approaches either rely on strong assumptions or lack generalization. Therefore, we propose a novel Sybil detection model based on generative adversarial networks (GANs), which contains a feature extractor, a domain classifier, and a Sybil detector. First, the feature extractor is proposed to identify the rich information in the review text with the neural network model of TextCNN. Second, the domain classifier is implemented by a neural network discriminator and is able to extract common features. Third, the Sybil detector is utilized to discriminate the fake review. Finally, the minimax game between the domain classifier and Sybil detector forms a GAN and enhances the overall generalization ability of the model. Extensive experiments show that our model has a high detection accuracy against Sybil attacks.