scholarly journals Comparing Manual and Automated Coding Methods of Nonverbal Synchrony

2020 ◽  
pp. 1-18
Author(s):  
Ken Fujiwara ◽  
Quinten S. Bernhold ◽  
Norah E. Dunbar ◽  
Christopher D. Otmar ◽  
Mohemmad Hansia
Keyword(s):  
Automated Coding ◽  
Nonverbal Synchrony

Patterns of early change in interpersonal problems and their relationship to nonverbal synchrony and multidimensional outcome.

2020 ◽  
Vol 67 (4) ◽  
pp. 449-461 ◽  
Author(s):  
Wolfgang Lutz ◽  
Jessica N. Prinz ◽  
Brian Schwartz ◽  
Jane Paulick ◽  
Desiree Schoenherr ◽  
...  
Keyword(s):  
Interpersonal Problems ◽  
Early Change ◽  
Nonverbal Synchrony

Automated Coding of Patient Discharge Summaries Using Conceptual Graphs

1995 ◽  
Vol 34 (04) ◽  
pp. 345-351 ◽  
Author(s):  
A. Burgun ◽  
L. P. Seka ◽  
D. Delamarre ◽  
P. Le Beux
Keyword(s):  
Natural Language ◽  
Patient Discharge ◽  
Natural Language Understanding ◽  
Assessment Procedure ◽  
Coding System ◽  
Language Understanding ◽  
Processing Scheme ◽  
Automated Coding ◽  
Conceptual Graph ◽  
Discharge Summaries

Abstract:In medicine, as in other domains, indexing and classification is a natural human task which is used for information retrieval and representation. In the medical field, encoding of patient discharge summaries is still a manual time-consuming task. This paper describes an automated coding system of patient discharge summaries from the field of coronary diseases into the ICD-9-CM classification. The system is developed in the context of the European AIM MENELAS project, a natural-language understanding system which uses the conceptual-graph formalism. Indexing is performed by using a two-step processing scheme; a first recognition stage is implemented by a matching procedure and a secondary selection stage is made according to the coding priorities. We show the general features of the necessary translation of the classification terms in the conceptual-graph model, and for the coding rules compliance. An advantage of the system is to provide an objective evaluation and assessment procedure for natural-language understanding.

scholarly journals Detection of eye contact with deep neural networks is as accurate as human experts

2020 ◽  
Author(s):  
Eunji Chong ◽  
Elysha Clark-Whitney ◽  
Audrey Southerland ◽  
Elizabeth Stubbs ◽  
Chanel Miller ◽  
...  
Keyword(s):  
Neural Network ◽  
Eye Contact ◽  
Autism Spectrum ◽  
Point Of View ◽  
Gaze Behavior ◽  
Equivalence Tests ◽  
Social Settings ◽  
Automated Coding ◽  
Primary Means ◽  
And Performance

Eye contact is among the most primary means of social communication that humans use from the first months of life. Quantification of eye contact is valuable in various scenarios as a part of the analysis of social roles, communication skills, and medical screening. Estimating a subject's looking direction from video is a challenging task, but eye contact can be effectively captured by a wearable point-of-view camera which provides a unique viewpoint as a result of its configuration. While moments of eye contact from this viewpoint can be hand coded, such process tends to be laborious and subjective. In this work, we developed the first deep neural network model to automatically detect eye contact in egocentric video with accuracy equivalent to that of human experts. We trained a deep convolutional neural network using a dataset of 4,339,879 annotated images, consisting of 103 subjects with diverse demographic backgrounds. 57 have a diagnosis of Autism Spectrum Disorder. The network achieves overall precision 0.936 and recall 0.943 on 18 set-aside validation subjects, and performance is on par with 10 trained human coders with a mean precision 0.918 and recall 0.946. This result passes class equivalence tests in Cohen’s kappa scores (equivalence boundary of 0.025, p < .005), demonstrating that deep learning model can produce automated coding with a level of reliability comparable to human coders. The presented method will be instrumental in analyzing gaze behavior in naturalistic social settings by serving as a scalable, objective, and accessible tool for clinicians and researchers.

scholarly journals The Dynamics of Issue Attention in Online Communication on Climate Change

2015 ◽  
Vol 3 (1) ◽  
pp. 17-33 ◽  
Author(s):  
Ines Lörcher ◽  
Irene Neverla
Keyword(s):  
Climate Change ◽  
Online Communication ◽  
Discussion Forums ◽  
The Public ◽  
Quantitative Content ◽  
Ipcc Report ◽  
Automated Coding ◽  
Issue Attention ◽  
Reader Comments ◽  
Shed Light

Issues and their sub-topics in the public agenda follow certain dynamics of attention. This has been studied for “offline” media, but barely for online communication. Furthermore, the enormous spectrum of online communication has not been taken into account. This study investigates whether specific dynamics of attention on issues and sub-topics can be found in different online public arenas. We expect to identify differences across various arenas as a result of their specific stakeholders and constellations of stakeholders, as well as different trigger events. To examine these assumptions, we shed light on the online climate change discourse in Germany by undertaking a quantitative content analysis via manual and automated coding methods of journalistic articles and their reader comments, scientific expert blogs, discussion forums and social media at the time of the release of the 5th IPCC report and COP19, both in 2013 (n = 14.582). Our results show online public <em>arena-specific dynamics</em> of issue attention and sub-topics. In journalistic media, we find more continuous issue attention, compared to a public arena where everyone can communicate. Furthermore, we find <em>event-specific dynamics</em> of issue attention and sub-topics: COP19 received intensive and continuous attention and triggered more variation in the sub-topics than the release of the IPCC report.

Nonverbal Synchrony and Complementarity in Unacquainted Same-Sex Dyads: A Comparison in a Competitive Context

2018 ◽  
Vol 42 (2) ◽  
pp. 179-197 ◽  
Author(s):  
Niclà Lozza ◽  
Corinne Spoerri ◽  
Ulrike Ehlert ◽  
Marion Kesselring ◽  
Priska Hubmann ◽  
...  
Keyword(s):  
Same Sex ◽  
Nonverbal Synchrony

Nonverbal synchrony as a marker of alliance ruptures.

Psychotherapy ◽  
2021 ◽  
Vol 58 (4) ◽  
pp. 499-509
Author(s):  
Keren Deres-Cohen ◽  
Tohar Dolev-Amit ◽  
Galit Peysachov ◽  
Fabian T. Ramseyer ◽  
Sigal Zilcha-Mano
Keyword(s):  
Alliance Ruptures ◽  
Nonverbal Synchrony

Using Computational Text Analysis to Explore Open-Ended Survey Question Responses

2019 ◽  
pp. 148-180
Keyword(s):  
Text Analysis ◽  
Online Survey ◽  
Survey Question ◽  
Software Applications ◽  
Text Structures ◽  
Automated Coding ◽  
External Software ◽  
Wide Availability ◽  
File Upload ◽  
Audio Video

To capture a broader range of data than close-ended questions (often defined and delimited by the survey instrument designer), open-ended questions, such as text-based elicitations (and file-upload options for still imagery, audio, video, and other contents) are becoming more common because of the wide availability of computational text analysis, both within online survey tools and in external software applications. These computational text analysis tools—some online, some offline—make it easier to capture reproducible insights with qualitative data. This chapter explores some analytical capabilities, in matrix queries, theme extraction (topic modeling), sentiment analysis, cluster analysis (concept mapping), network text structures, qualitative cross-tabulation analysis, manual coding to automated coding, linguistic analysis, psychometrics, stylometry, network analysis, and others, as applied to open-ended questions from online surveys (and combined with human close reading).

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