7,263 research outputs found

    Learning to predict closed questions on stack overflow

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    The paper deals with the problem of predicting whether the user’s question will be closed by the moderator on Stack Overflow, a popular question answering service devoted to software programming. The task along with data and evaluation metrics was offered as an open machine learning competition on Kaggle platform. To solve this problem, we employed a wide range of classification features related to users, their interactions, and post content. Classification was carried out using several machine learning methods. According to the results of the experiment, the most important features are characteristics of the user and topical features of the question. The best results were obtained using Vowpal Wabbit – an implementation of online learning based on stochastic gradient descent. Our results are among the best ones in overall ranking, although they were obtained after the official competition was over

    Lip(s) Service: A Socioethical Overview of Social Media Platforms’ Censorship Policies Regarding Consensual Sexual Content

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    The regulation of sexual exploitation on social media is a pressing issue that has been addressed by government legislation. However, laws such as FOSTA-SESTA has inadvertently restricted consensual expressions of sexuality as well. In four social media case studies, this paper investigates the ways in which marginalized groups have been impacted by changing censorship guidelines on social media, and how content moderation methods can be inclusive of these groups. I emphasize the qualitative perspectives of sex workers and queer creators in these case studies, in addition to my own experiences as a content moderation and social media management intern for Lips.social. This paper concludes with potential solutions to current biases in social media content moderation

    Recovering Tech\u27s Humanity

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    The neighbourhood physical environment and active travel in older adults : a systematic review and meta-analysis

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    BACKGROUND: Perceived and objectively-assessed aspects of the neighbourhood physical environment have been postulated to be key contributors to regular engagement in active travel (AT) in older adults. We systematically reviewed the literature on neighbourhood physical environmental correlates of AT in older adults and applied a novel meta-analytic approach to statistically quantify the strength of evidence for environment-AT associations. METHODS: Forty two quantitative studies that estimated associations of aspects of the neighbourhood built environment with AT in older adults (aged ≥ 65 years) and met selection criteria were reviewed and meta-analysed. Findings were analysed according to five AT outcomes (total walking for transport, within-neighbourhood walking for transport, combined walking and cycling for transport, cycling for transport, and all AT outcomes combined) and seven categories of the neighbourhood physical environment (residential density/urbanisation, walkability, street connectivity, access to/availability of services/destinations, pedestrian and cycling infrastructure, aesthetics and cleanliness/order, and safety and traffic). RESULTS: Most studies examined correlates of total walking for transport. A sufficient amount of evidence of positive associations with total walking for transport was found for residential density/urbanisation, walkability, street connectivity, overall access to destinations/services, land use mix, pedestrian-friendly features and access to several types of destinations. Littering/vandalism/decay was negatively related to total walking for transport. Limited evidence was available on correlates of cycling and combined walking and cycling for transport, while sufficient evidence emerged for a positive association of within-neighbourhood walking with pedestrian-friendly features and availability of benches/sitting facilities. Correlates of all AT combined mirrored those of walking for transport. Positive associations were also observed with food outlets, business/institutional/industrial destinations, availability of street lights, easy access to building entrance and human and motorised traffic volume. Several but inconsistent individual- and environmental-level moderators of associations were identified. CONCLUSIONS: Results support strong links between the neighbourhood physical environment and older adults’ AT. Future research should focus on the identification of types and mixes of destinations that support AT in older adults and how these interact with individual characteristics and other environmental factors. Future research should also aim to clarify dose-response relationships through multi-country investigations and data-pooling from diverse geographical regions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12966-017-0471-5) contains supplementary material, which is available to authorized users

    Rulemaking 2.0

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    In response to President Obama\u27s Memorandum on Transparency and Open Government, federal agencies are on the verge of a new generation in online rulemaking. However, unless we recognize the several barriers to making rulemaking a more broadly participatory process, and purposefully adapt Web 2.0 technologies and methods to lower those barriers, Rulemaking 2.0 is likely to disappoint agencies and open-government advocates alike. This article describes the design, operation, and initial results of Regulation Room, a pilot public rulemaking participation platform created by a cross-disciplinary group of Cornell researchers in collaboration with the Department of Transportation. Regulation Room uses selected live rulemakings to experiment with human and computer support for public comment. The ultimate project goal is to provide guidance on design, technological, and human intervention strategies, grounded in theory and tested in practice, for effective Rulemaking 2.0 systems. Early results give some cause for optimism about the open-government potential of Web 2.0-supported rulemaking. But significant challenges remain. Broader, better public participation is hampered by 1) ignorance of the rulemaking process; 2) unawareness that rulemakings of interest are going on; and 3) information overload from the length and complexity of rulemaking materials. No existing, commonly used Web services or applications are good analogies for what a Rulemaking 2.0 system must do to lower these barriers. To be effective, the system must not only provide the right mix of technology, content, and human assistance to support users in the unfamiliar environment of complex government policymaking; it must also spur them to revise their expectations about how they engage information on the Web and also, perhaps, about what is required for civic participation

    Rulemaking 2.0

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    How to improve compliance with protective health measures during the covid-19 outbreak. Testing a moderated mediation model and machine learning algorithms

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    In the wake of the sudden spread of COVID-19, a large amount of the Italian population practiced incongruous behaviors with the protective health measures. The present study aimed at examining psychological and psychosocial variables that could predict behavioral compliance. An online survey was administered from 18–22 March 2020 to 2766 participants. Paired sample t-tests were run to compare efficacy perception with behavioral compliance. Mediation and moderated mediation models were constructed to explore the association between perceived efficacy and compliance, mediated by self-efficacy and moderated by risk perception and civic attitudes. Machine learning algorithms were trained to predict which individuals would be more likely to comply with protective measures. Results indicated significantly lower scores in behavioral compliance than efficacy perception. Risk perception and civic attitudes as moderators rendered the mediating effect of self-efficacy insignificant. Perceived efficacy on the adoption of recommended behaviors varied in accordance with risk perception and civic engagement. The 14 collected variables, entered as predictors in machine learning models, produced an ROC area in the range of 0.82–0.91 classifying individuals as high versus low compliance. Overall, these findings could be helpful in guiding age-tailored information/advertising campaigns in countries affected by COVID-19 and directing further research on behavioral compliance

    Youtube is Unsafe for Children: Youtube\u27s Safeguards and the Current Legal Framework are Inadequate to Protect Children from Disturbing Content

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    For America’s children, the amount of screen time they consume has not changed much over the years. Children under eight have steadily spent about two hours a day in front of a screen, with those under age two averaging 42 minutes a day. Children from low-income families spend roughly an hour and forty minutes longer in front of a screen. According to the American Academy of Pediatrics, screen time should be limited to two hours a day for children ages two to five; whereas, for those youngest children—under two years—they recommend zero screen time. While the average amount of screen time has remained constant over the years, the medium used during such screen time has rapidly shifted from the television to mobile devices. Screen media consumption on a mobile device used to occupy only 4% of a child’s screen time; in 2017, it grew to 35% The increasing prevalence of mobile devices (smartphones and tablets) in the home certainly explains this change in screen time habits. In 2011, less than 1% of children under the age of eight had their own tablet device.In 2013, the number rose to 7%, and by 2017, that number had skyrocketed to 42%. Over the past decade, YouTube has both created and taken over the online video streaming market. However, the company has grown so rapidly, and the platform is so large and uncontrollable, that YouTube is struggling to keep inappropriate content from children
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