ResearchPad - computer-sciences https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[Estimating geographic subjective well-being from Twitter: A comparison of dictionary and data-driven language methods]]> https://www.researchpad.co/article/elastic_article_8297 Researchers and policy makers worldwide are interested in measuring the subjective well-being of populations. When users post on social media, they leave behind digital traces that reflect their thoughts and feelings. Aggregation of such digital traces may make it possible to monitor well-being at large scale. However, social media-based methods need to be robust to regional effects if they are to produce reliable estimates. Using a sample of 1.53 billion geotagged English tweets, we provide a systematic evaluation of word-level and data-driven methods for text analysis for generating well-being estimates for 1,208 US counties. We compared Twitter-based county-level estimates with well-being measurements provided by the Gallup-Sharecare Well-Being Index survey through 1.73 million phone surveys. We find that word-level methods (e.g., Linguistic Inquiry and Word Count [LIWC] 2015 and Language Assessment by Mechanical Turk [LabMT]) yielded inconsistent county-level well-being measurements due to regional, cultural, and socioeconomic differences in language use. However, removing as few as three of the most frequent words led to notable improvements in well-being prediction. Data-driven methods provided robust estimates, approximating the Gallup data at up to r = 0.64. We show that the findings generalized to county socioeconomic and health outcomes and were robust when poststratifying the samples to be more representative of the general US population. Regional well-being estimation from social media data seems to be robust when supervised data-driven methods are used.

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<![CDATA[Vulnerable robots positively shape human conversational dynamics in a human–robot team]]> https://www.researchpad.co/article/Naa6d91ed-903f-40e8-9d97-72528b57a6b6

Significance

Prior work has demonstrated that a robot’s social behavior has the ability to shape people’s trust toward, responses to, and impressions of a robot within human–robot interactions. However, when the context changes to interactions within a group involving one robot and multiple people, the influence of the robot on group behavior is less well understood. In this work, we explore how a social robot influences team engagement using an experimental design where a group of three humans and one robot plays a collaborative game. Our analysis shows that a robot’s social behavior influences the conversational dynamics between human members of the human–robot group, demonstrating the ability of a robot to significantly shape human–human interaction.

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<![CDATA[Assessing the reliability of a clothing-based forensic identification]]> https://www.researchpad.co/article/Ned54c136-8216-404d-a6d0-385decec1005

Significance

Our justice system relies critically on the use of forensic science. More than a decade ago, a highly critical report raised significant concerns as to the reliability of many forensic techniques. These concerns persist today. Of particular concern to us is the use of photographic pattern analysis that attempts to identify an individual from purportedly distinct features. Such techniques have been used extensively in the courts over the past half century without, in our opinion, proper validation. We propose, therefore, that a large class of these forensic techniques should be subjected to rigorous analysis to determine their efficacy and appropriateness in the identification of individuals.

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<![CDATA[A scalable pipeline for designing reconfigurable organisms]]> https://www.researchpad.co/article/Nc9e7d236-5413-445e-a629-a619b43087b7

Significance

Most technologies are made from steel, concrete, chemicals, and plastics, which degrade over time and can produce harmful ecological and health side effects. It would thus be useful to build technologies using self-renewing and biocompatible materials, of which the ideal candidates are living systems themselves. Thus, we here present a method that designs completely biological machines from the ground up: computers automatically design new machines in simulation, and the best designs are then built by combining together different biological tissues. This suggests others may use this approach to design a variety of living machines to safely deliver drugs inside the human body, help with environmental remediation, or further broaden our understanding of the diverse forms and functions life may adopt.

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<![CDATA[Bots increase exposure to negative and inflammatory content in online social systems]]> https://www.researchpad.co/article/5c26b51fd5eed0c484764c21

Significance

Social media can deeply influence reality perception, affecting millions of people’s voting behavior. Hence, maneuvering opinion dynamics by disseminating forged content over online ecosystems is an effective pathway for social hacking. We propose a framework for discovering such a potentially dangerous behavior promoted by automatic users, also called “bots,” in online social networks. We provide evidence that social bots target mainly human influencers but generate semantic content depending on the polarized stance of their targets. During the 2017 Catalan referendum, used as a case study, social bots generated and promoted violent content aimed at Independentists, ultimately exacerbating social conflict online. Our results open challenges for detecting and controlling the influence of such content on society.

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<![CDATA[Psychometric data of a questionnaire to measure cyberbullying bystander behavior and its behavioral determinants among adolescents]]> https://www.researchpad.co/article/5b5abed1463d7e0e3ef9376e

.This paper describes the items, scale validity and scale reliability of a self-report questionnaire that measures bystander behavior in cyberbullying incidents among adolescents, and its behavioral determinants. Determinants included behavioral intention, behavioral attitudes, moral disengagement attitudes, outcome expectations, self-efficacy, subjective norm and social skills. Questions also assessed (cyber-)bullying involvement. Validity and reliability information is based on a sample of 238 adolescents (M age=13.52 years, SD=0.57). Construct validity was assessed using Confirmatory Factor Analysis (CFA) or Exploratory Factor Analysis (EFA) in Mplus7 software. Reliability (Cronbach Alpha, α) was assessed in SPSS, version 22. Data and questionnaire are included in this article. Further information can be found in DeSmet et al. (2018) [1].

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