Crisis ◽  
2017 ◽  
Vol 38 (3) ◽  
pp. 207-209 ◽  
Author(s):  
Florian Arendt ◽  
Sebastian Scherr

Abstract. Background: Research has already acknowledged the importance of the Internet in suicide prevention as search engines such as Google are increasingly used in seeking both helpful and harmful suicide-related information. Aims: We aimed to assess the impact of a highly publicized suicide by a Hollywood actor on suicide-related online information seeking. Method: We tested the impact of the highly publicized suicide of Robin Williams on volumes of suicide-related search queries. Results: Both harmful and helpful search terms increased immediately after the actor's suicide, with a substantial jump of harmful queries. Limitations: The study has limitations (e.g., possible validity threats of the query share measure, use of ambiguous search terms). Conclusion: Online suicide prevention efforts should try to increase online users' awareness of and motivation to seek help, for which Google's own helpline box could play an even more crucial role in the future.


2012 ◽  
Author(s):  
Linda M. Woolf ◽  
Michael R. Hulsizer ◽  
Danielle Maccartney

2020 ◽  
Vol 158 (3) ◽  
pp. S86
Author(s):  
Scott Baumgartner ◽  
Vinay Rao ◽  
Ali Khan ◽  
Marie Borum
Keyword(s):  

Author(s):  
Padmavathi .S ◽  
M. Chidambaram

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.


2017 ◽  
Vol 9 (2) ◽  
pp. 67-71
Author(s):  
Herru Darmadi ◽  
Yan Fi ◽  
Hady Pranoto

Learning Object (LO) is a representation of interactive content that are used to enrich e-learning activities. The goals of this case study were to evaluate accessibility and compatibility factors from learning objects that were produced by using BINUS E-learning Authoring Tool. Data were compiled by using experiment to 30 learning objects by using stratified random sampling from seven faculties in undergraduate program. Data were analyzed using accessibility and compatibility tests based on Web Content Accessibility Guidelines 2.0 Level A. Results of the analysis for accessibility and compatibility tests of Learning Objects was 90% better than average. The result shows that learning objects is fully compatible with major web browser. This paper also presents five accessibility problems found during the test and provide recommendation to overcome the related problems. It can be concluded that the learning objects that were produced using BINUS E-learning Authoring Tool have a high compatibility, with minor accessibility problems. Learning objects with a good accessibility and compatibility will be beneficial to all learner with or without disabilities during their learning process. Index Terms—accessibility, compatibility, HTML, learning object, WCAG2.0, web


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