Flud: A Hybrid Crowd–Algorithm Approach for Visualizing Biological Networks

Modern experiments in many disciplines generate large quantities of network (graph) data. Researchers require aesthetic layouts of these networks that clearly convey the domain knowledge and meaning. However, the problem remains challenging due to multiple conflicting aesthetic criteria and complex domain-specific constraints. In this article, we present a strategy for generating visualizations that can help network biologists understand the protein interactions that underlie processes that take place in the cell. Specifically, we have developed Flud, a crowd-powered system that allows humans with no expertise to design biologically meaningful graph layouts with the help of algorithmically generated suggestions. Furthermore, we propose a novel hybrid approach for graph layout wherein crowd workers and a simulated annealing algorithm build on each other’s progress. A study of about 2,000 crowd workers on Amazon Mechanical Turk showed that the hybrid crowd–algorithm approach outperforms the crowd-only approach and state-of-the-art techniques when workers were asked to lay out complex networks that represent signaling pathways. Another study of seven participants with biological training showed that Flud layouts are more effective compared to those created by state-of-the-art techniques. We also found that the algorithmically generated suggestions guided the workers when they are stuck and helped them improve their score. Finally, we discuss broader implications for mixed-initiative interactions in layout design tasks beyond biology.

Promoting engagement with social fact-checks online

Social corrections, wherein social media users correct one another, are an important mechanism for debunking online misinformation. But users who post misinformation only rarely engage with social corrections, instead typically choosing to ignore them. Here, we investigate how the social relationship between the corrector and corrected user affect the willingness to engage with corrective, debunking messages. We explore two key dimensions: (i) partisan agreement with, and (ii) social relationships between the user and the corrector. We conducted a randomized field experiment with Twitter users and a conceptual replication survey experiment with Amazon Mechanical Turk workers in which posts containing false news were corrected. We varied whether the corrector identified as a Democrat or Republican; and whether the corrector followed the user and liked three of their tweets the day before issuing the correction (creating a minimal social relationship). Surprisingly, we did not find evidence that shared partisanship increased a user’s probability of engaging with the correction. Conversely, forming a minimal social connection significantly increased engagement rate. A second survey experiment found that minimal social relationships foster a general norm of responding, such that people feel more obligated to respond – and think others expect them to respond more – to people who follow them, even outside the context of misinformation correction. These results emphasize social media’s ability to foster engagement with corrections via minimal social relationships, and have implications for effective, engaging fact-check delivery online.

Doubly Robust Crowdsourcing

Large-scale labeled dataset is the indispensable fuel that ignites the AI revolution as we see today. Most such datasets are constructed using crowdsourcing services such as Amazon Mechanical Turk which provides noisy labels from non-experts at a fair price. The sheer size of such datasets mandates that it is only feasible to collect a few labels per data point. We formulate the problem of test-time label aggregation as a statistical estimation problem of inferring the expected voting score. By imitating workers with supervised learners and using them in a doubly robust estimation framework, we prove that the variance of estimation can be substantially reduced, even if the learner is a poor approximation. Synthetic and real-world experiments show that by combining the doubly robust approach with adaptive worker/item selection rules, we often need much lower label cost to achieve nearly the same accuracy as in the ideal world where all workers label all data points.

An informed approach to the development of primary care pediatric firearm safety messages

Background Firearm ownership is prevalent in the US and many children spend time in areas where firearms are not stored safely. The AAP recommends firearm safety counseling at pediatric well-visits. Methods We developed and tested six contextual messages to promote safe firearm storage based on: absence of harm, collective appeal to understanding child behavior, pediatrician’s authority, evidence-based, fear appeal, and general safety considerations. One hundred four parents who keep firearms at home were recruited from Amazon Mechanical Turk Prime and viewed video messages and reported behavioral intentions and emotional reactions following each message. Results All six contextual messages were perceived as important and believable and increased parents’ intentions to follow safety advice provided, but also elicited negative emotions. The authority message elicited more negative emotions and resulted in lower intentions to follow safe storage advice. Conclusions Including firearm messages with other child safety advice merits further evaluation. Authority messages should be avoided.

An Experimental Test of the Impact of Varying Questionnaire Response Format on Prevalence Rates for Sexual Violence Victimization and Perpetration

Objective: This study examined the impact of a dichotomous versus scaled response format on prevalence rates of sexual violence perpetration and victimization, thus conceptually replicating Hamby et al., 2006 and extending those findings to the context of sexual violence. Methods: Two samples were recruited from Amazon Mechanical Turk and were randomly assigned to either a dichotomous or scaled response format of the same questionnaire. Sample 1 was used to examine perpetration and received a perpetration specific version of the Post-Refusal Sexual Persistence Scale (PRSPS). Sample 2 was used to examine victimization and received a victimization specific version of the PRSPS. Results: Men and women who received the scaled response format reported significantly more incidents of sexual perpetration, and for each type of tactic studied, on the order of 1.7-9x more frequently. The association between response format condition and prevalence rates of sexual violence victimization was significant only for men (23.0 vs 39.4%) and for the tactic of verbal coercion (30.1 vs 41.5%), with the scaled response format producing greater responding. Conclusions: The response format of sexual violence items can significantly alter prevalence rates of sexual violence perpetration, with scaled response formats producing greater endorsements than dichotomous formats. Response format also appears to impact prevalence rates of sexual violence victimization, particularly for men.

Overcoming organizational politics with tenacity and passion for work: benefits for helping behaviors

PurposeThis study unpacks the relationship between employees' perceptions of organizational politics and their helping behavior, by explicating a mediating role of employees' affective commitment and moderating roles of their tenacity and passion for work.Design/methodology/approachQuantitative survey data were collected from 476 employees, through Amazon Mechanical Turk.FindingsBeliefs that the organizational climate is predicated on self-serving behaviors diminish helping behaviors, and this effect arises because employees become less emotionally attached to their organization. This mediating role of affective commitment is less salient to the extent that employees persevere in the face of challenges and feel passionate about working hard.Practical implicationsFor human resource managers, this study pinpoints a lack of positive organization-oriented energy as a key mechanism by which perceptions about a negative political climate steer employees away from assisting organizational colleagues on a voluntary basis. They can contain this mechanism by ensuring that employees are equipped with energy-boosting personal resources.Originality/valueThis study addresses employees' highly salient emotional reactions to organizational politics and pinpoints the critical function of affective commitment for explaining the escalation of perceived organizational politics into diminished helping behavior. It also identifies buffering effects linked to two pertinent personal resources.

A survey study of adults with chronic conditions: Examining the correlation between patient activation and health locus of control

Objectives This study aimed to examine (1) the association between patient activation (PA), health locus of control (HLOC), sociodemographic and clinical factors, and (2) the effect of HLOC dimensions, sociodemographic and clinical factors on PA. Methods Three hundred U.S. adults, with at least one chronic condition (CC) were recruited through Amazon Mechanical Turk and completed an online survey which included sociodemographic questions, the Patient Activation Measure® - 10, and the Multidimensional Locus of Control (MHLC) - Form B. Statistical analyses, including descriptive, correlation, and multiple linear regression, were conducted using IBM SPSS v25. Results Of the 300 participants, more than half were male (66.3%), White (70.7%), with at least a college degree (76.0%), and employed full-time (79.0%). The average PA score was 68.8 ± 14.5. Multiple linear regression indicated that participants who reported they were Black, retired, with a greater number of CCs, and with higher scores in Chance MHLC had higher PA, while participants with higher scores in Internal MHLC, were unemployed and reported to have been affected by COVID-19-related worry or fear to manage their CC, had lower PA. Discussion HLOC dimensions should be addressed concurrently with PA for patients with CCs, thus adding to a more patient-centered clinical approach.

The Impact of COVID-19 on Travelers’ Travel Behavior

The purpose of this paper is to investigate the impact of a global pandemic, Coronavirus (COVID-19), on travel behavior by integrating crisis management with other behavior variables. Online surveys via the Amazon Mechanical Turk website were used for data collection. A total of 1978 responses were collected from 19 countries (September, 2020); 1607 valid cases were undertaken for the data analysis. The results suggest that crisis management intervention was the most influential factor impacting travel behavior during the COVID-19 pandemic. Non-pharmaceutical interventions followed as the second most influencing predictor. . Personal traits were the only variable that recorded a negative relationship with travel behavior. This study has significant practical and theoretical implications since it adopted the extended model of goal-directed behavior while including crisis management intervention, which is a unique approach.  Although attitudes generally strongly influence travel behavior, in a pandemic situation, such relationships weaken. In terms of practical implications, it has become apparent that firms and governments should implement integrated risk management and crisis management strategies. Travelers need to be reassured that these tourist destinations have become safe again. This will likely decrease the risk factor, therefore influencing them to travel to this region. This study contributes to understanding how a global pandemic influences travel behavior It will be invaluable in predicting the early reflections of travelers as the pandemic ends.

Viewing Sports Online during the COVID-19 Pandemic: The Antecedent Effects of Social Presence on the Technology Acceptance Model

The outbreak of COVID-19 in 2020 has posed several challenges to the sporting industry, caused by the change in behavior of sporting fans from purchasing event tickets to watching live broadcasts of events on the Internet. This study aims to gain a deeper understanding of fan behavior in this “new normal”. It adopts a technology acceptance model (TAM) to explore the effects of social presence (SP) in the context of online viewing of professional sports. For this purpose, the authors conducted an online survey of viewers who watched NBA sports events on the Amazon Mechanical Turk website using the Together Mode feature of Microsoft Teams. We collected 209 valid questionnaires and performed a partial least squares structural equation modeling analysis. The results showed that the SP-TAM structural model has adequate predictive relevance, and SP has a statistically significant positive relationship with both perceived ease of use and perceived usefulness. The model was thus validated, contributing to the existing body of knowledge on emerging technologies such as the creation of a virtual audience in sports. The study’s findings suggest that technology developers should focus on the effects of SP and emphasize practical functions to increase the use intention of sporting fans. Furthermore, professional sporting leagues should prioritize the use of virtual fan technology to optimize the viewing experience of their fans.