Networks of Gratitude: Structures of Thanks and User Expectations in Workplace Appreciation Systems

Appreciation systems--platforms for users to exchange thanks and praise--are becoming common in the workplace, where employees share appreciation, managers are notified, and aggregate scores are sometimes made visible. Who do people thank on these systems, and what do they expect from each other and their managers? After introducing the design affordances of 13 appreciation systems, we discuss a system we call Gratia, in use at a large multinational company for over four years. Using logs of 422,000 appreciation messages and user surveys, we explore the social dynamics of use and ask if use of the system addresses the recognition problem. We find that while thanks is mostly exchanged among employees at the same level and different parts of the company, addressing the recognition problem, managers do not always act on that recognition in ways that employees expect.Comment: in Tenth International AAAI Conference on Web and Social Media, 201

Searching for Community Online: How Virtual Spaces Affect Student Notions of Community

Social networking sites and virtual spaces have flourished in the past few years. The author explores the impact of such social networking services on the local community at a small liberal arts college. The author investigates modern trends in community theory. Defining community has become more difficult in modern society, where community is no longer easily distinguished by geographical boundaries. From the background of modern community theory the author explores the designation of virtual spaces as “virtual communities.” Literature and research about virtual spaces indicates that they can provide many of the values thought be to inherent to community membership. The strong localized community on campus makes students hesitant in calling Facebook a “virtual community,” despite its strong integration with the face-to-face community itself. Facebook is seen as simply a tool. This thesis incorporates research on one specific case study: through mathematical and ethnographic research of Facebook.com, the author evaluates the opinions of students in considering virtual spaces as communities

Friending in Online Fitness Communities: Exploring Activity-Based Online Network Structure

Individuals are influenced by both direct and indirect interaction with their social contacts. While peer influence is known to affect health-related outcomes such as exercise, limited work has fully explored how social networks are structured to support (or inhibit) interaction that could lead to positive health behaviors. With the development of pervasive technology and rise of personal health and wellness tracking, increasing attention has been paid to promoting positive fitness behaviors through social interaction mechanisms in online fitness communities. This trend offers a unique opportunity to understand the opportunity structures for personal health and wellness support. Utilizing a large-scale behavioral trace dataset from the online fitness community Strava, we examine how the size of people\u27s personal network is structured by demographics (e.g. gender and age) and an economic indicator (i.e. if they pay for a premium account). We employ stochastic process models to characterize the empirical network degree distributions in this population of fitness community members. We find that gender, age and account status are associated with distinct network structure. Results have implications in the analysis and the design of health interventions that make use of network relationships in online settings

Exploring Effective Ecosystems in Disaster Management: Case studies of Japan and Nepal

Existing literature argues that taking a holistic approach to disaster management is important for organizations in achieving resilience. However, theoretical underpinnings are lacking to achieve a holistic understanding. This paper applies the notion of an ecosystem as a holistic lens to understand complex disaster management. \ \ We report two case studies from Japan and Nepal to illustrate how an ecosystem works during a disaster. The Japan case is a government initiative, whereas the Nepal case is a non-governmental initiative. The theoretical framework of information ecology is used in analyzing the cases. \ \ Based on the findings, we formulate three propositions that show important elements of ecosystems to approach resilience. The study suggests that coevolution is a key to respond to constantly changing situations during a disaster. To accomplish ecosystem coevolution, creating a collaboration system with governments and local communities and embedding local knowledge into the system are essential. Furthermore, digital tools can play a critical role in the coevolution process.

Followback Clusters, Satellite Audiences, and Bridge Nodes: Coengagement Networks for the 2020 US Election

The 2020 United States presidential election was, and has continued to be, the focus of pervasive and persistent mis- and disinformation spreading through our media ecosystems, including social media. This event has driven the collection and analysis of large, directed social network datasets, but such datasets can resist intuitive understanding. In such large datasets, the overwhelming number of nodes and edges present in typical representations create visual artifacts, such as densely overlapping edges and tightly-packed formations of low-degree nodes, which obscure many features of more practical interest. We apply a method, coengagement transformations, to convert such networks of social data into tractable images. Intuitively, this approach allows for parameterized network visualizations that make shared audiences of engaged viewers salient to viewers. Using the interpretative capabilities of this method, we perform an extensive case study of the 2020 United States presidential election on Twitter, contributing an empirical analysis of coengagement. By creating and contrasting different networks at different parameter sets, we define and characterize several structures in this discourse network, including bridging accounts, satellite audiences, and followback communities. We discuss the importance and implications of these empirical network features in this context. In addition, we release open-source code for creating coengagement networks from Twitter and other structured interaction data.Comment: Accepted for publication at ICWSM '2

SN 2009ip at late times - an interacting transient at+2 years

We present photometric and spectroscopic observations of the interacting transient SN 2009ip taken during the 2013 and 2014 observing seasons. We characterize the photometric evolution as a steady and smooth decline in all bands, with a decline rate that is slower than expected for a solely Co-56-powered supernova at late phases. No further outbursts or eruptions were seen over a two year period from 2012 December until 2014 December. SN 2009ip remains brighter than its historic minimum from pre-discovery images. Spectroscopically, SN 2009ip continues to be dominated by strong, narrow (less than or similar to 2000 km s(-1)) emission lines of H, He, Ca, and Fe. While we make tenuous detections of [Fe II] lambda 7155 and [O I] lambda lambda 6300, 6364 lines at the end of 2013 June and the start of 2013 October, respectively, we see no strong broad nebular emission lines that could point to a core-collapse origin. In general, the lines appear relatively symmetric, with the exception of our final spectrum in 2014 May, when we observe the appearance of a redshifted shoulder of emission at +550 km s(-1). The lines are not blueshifted, and we see no significant near-or mid-infrared excess. From the spectroscopic and photometric evolution of SN 2009ip until 820 d after the start of the 2012a event, we still see no conclusive evidence for core-collapse, although whether any such signs could be masked by ongoing interaction is unclear

Let's Workout! Exploring Social Exercise in an Online Fitness Community

Increasing attention has been paid to promoting certain healthy habits through social interaction in online communities. At the intersection of social media and activity tracking applications, these platforms capture information on physical activities as well as peer-to-peer interactions. Importantly, they also offer researchers a novel opportunity to understand health behaviors by utilizing the large-scale behavioral trace data they archive. In this study we explore the characteristics and dynamics of social exercise (i.e. fitness activities with at least one peer physically co-present) using data collected from an online fitness community popular with cyclists and runners. In particular, we ask if factors such as temporal seasonality, activity performance and social feedback vary by the number of people participating in an activity; we do so by comparing associations for both men and women. Our results indicate that when peers are physically co-present for fitness activities (i.e. group workouts), exercise tends to be more intense and receive more feedback from other users, across both genders. Findings also suggest gender differences in the observed tendency to complete activities with others. These results have important implications for health and wellness interventions

Australian clinical practice guidelines for the diagnosis and management of Barrett's esophagus and early esophageal adenocarcinoma

Author version made available following 12 month embargo from date of publication according to publisher copyright policy.Barrett's esophagus (BE), a common condition, is the only known precursor to esophageal adenocarcinoma (EAC). There is uncertainty about the best way to manage BE as most people with BE never develop EAC and most patients diagnosed with EAC have no preceding diagnosis of BE. Moreover, there have been recent advances in knowledge and practice about the management of BE and early EAC. To aid clinical decision making in this rapidly moving field, Cancer Council Australia convened an expert working party to identify pertinent clinical questions. The questions covered a wide range of topics including endoscopic and histological definitions of BE and early EAC; prevalence, incidence, natural history, and risk factors for BE; and methods for managing BE and early EAC. The latter considered modification of lifestyle factors; screening and surveillance strategies; and medical, endoscopic, and surgical interventions. To answer each question, the working party systematically reviewed the literature and developed a set of recommendations through consensus. Evidence underpinning each recommendation was rated according to quality and applicability

Using Facebook Data to Examine Culture and Self-Disclosure Behaviors

In this work-in-progress poster, we examine the relationship between societal variables, including cultural attributes, and users' self-disclosure on Facebook. To accomplish this we use a dataset of 425,000 Facebook users who designated a national or regional network. Drawing on both standard demographic control variables and the GLOBE cultural dimensions, we execute an exhaustive model search. The best-performing model confirms our hypotheses about cultural variables, but some of our hypotheses about demographic controls are negated. Consequently, we discuss directions in which to continue our research.ye

ElectionRumors2022: A Dataset of Election Rumors on Twitter During the 2022 U.S. Midterms

Understanding the spread of online rumors is a pressing societal challenge and an active area of research across domains. In the context of the 2022 U.S. midterm elections, one influential social media platform for sharing information — including rumors that may be false, misleading, or unsubstantiated — was Twitter (now renamed X). To increase understanding of the dynamics of online rumors about elections, we present and analyze a dataset of 1.81 million Twitter posts corresponding to 135 distinct rumors which spread online during the midterm election season (September 5 to December 1, 2022). We describe how this data was collected, compiled, and supplemented, and provide a series of exploratory analyses along with comparisons to a previously published dataset on 2020 election rumors. We also conduct a mixed-methods analysis of five distinct rumors about the election in Arizona, a particularly prominent focus of 2022 election rumoring. Finally, we provide a set of potential future directions for how this dataset could be used to facilitate future research into online rumors, misinformation, and disinformation