Web Forum Launched for Schizophrenia Researchers

2005 ◽  
Author(s):  
Keyword(s):  
Web Forum

Web Forum Retrieval and Text Analytics: A Survey

2018 ◽  
Author(s):  
Doris Hoogeveen ◽  
Li Wang ◽  
Timothy Baldwin ◽  
Karin M. Verspoor
Keyword(s):  
Text Analytics ◽  
Web Forum

Web-forum as a speech genre of electronic communication

2016 ◽  
pp. 120-125
Author(s):  
Yuliya Aleksandrovna Miheeva ◽  
Keyword(s):  
Electronic Communication ◽  
Web Forum ◽  
Speech Genre

Alternative tourism and civil society in Athens

2019 ◽  
Vol 5 (1) ◽  
pp. 51-62
Author(s):  
Hecate Vergopoulos
Keyword(s):  
Civil Society ◽  
Economic Crisis ◽  
Design Methodology ◽  
Ethical Issues ◽  
Content Type ◽  
Alternative Tourism ◽  
Political Perspective ◽  
Web Forum ◽  
Guided Tours ◽  
Do So

Purpose The purpose of this paper is to tackle the issue of the meaning of tourism as it is being crippled by the economic crisis in Greece. Design/methodology/approach To do so, it brings together the findings of three different fieldworks related to tourism in Athens in times of crisis. Each one of these focuses on a specific player of tourism: a linguistic and semiological analysis led mainly on travel guides and ad campaigns deals with the industry of tourism; a linguistic analysis of tourists’ posts on a French web forum deals with the tourists themselves; and an ethnographical approach of alternative guided tours of Athens focuses on local players (associations and cooperatives offering out of the beaten tracks tours). Findings The whole study reveals that there is a misunderstanding between the industry and the consumers toward what the tourist practice should mean: whereas the tourists are in search of an ethical meaning, the industry claims there is no room for such issues. The alternative players, however, offer a political perspective that embraces the ethical issues raised by tourists. Originality/value They thus might, in the end, show us the way a so-called “civil society” could also have its own role to perform in tourism.

The Collaborative Critical Incident Tool

2004 ◽  
pp. 216-243 ◽  
Author(s):  
John M. Carroll ◽  
Dennis C. Neale ◽  
Phillip L. Isenhour
Keyword(s):  
Critical Incident ◽  
Communication Technologies ◽  
Evaluation Tool ◽  
Threaded Discussions ◽  
Computer Mediated ◽  
Web Forum ◽  
Incident Reports ◽  
K 12 ◽  
The Impact ◽  
Education Standard

We describe an evaluation tool used by teachers and researchers to study the impact of computer-mediated collaborative and communication technologies used in K-12 education. Standard usability engineering methods and tools focus on individual users at a single workstation.  Networked collaborative systems, however, present the challenge of multiple users interacting at a variety of times and places. We developed a Web forum tool to capture and display user critical incident reports and threaded discussions of these reports by users, evaluators and system developers. Our Collaborative Critical Incident Tool (CCIT) is effective at evoking detailed usability evaluation information, as well as reflective analysis of usability issues from diverse points of view among stakeholders in the system. 

Classroom Critical Incidents

2011 ◽  
pp. 275-281
Author(s):  
John M. Carroll ◽  
Dennis C. Neale ◽  
Philip L. Isenhour
Keyword(s):  
Critical Incident ◽  
Critical Incidents ◽  
Evaluation Method ◽  
User Interaction ◽  
Common Source ◽  
Incident Report ◽  
Web Forum ◽  
Web Forums ◽  
Human Participants

Evaluating the quality and effectiveness of user interaction in networked collaborative systems is difficult. There is more than one user, and often the users are not physically proximal. The “session” to be evaluated cannot be comprehensively observed or monitored at any single display, keyboard, or processor. It is typical that none of the human participants has an overall view of the interaction (a common source of problems for such interactions). The users are not easily accessible either to evaluators or to one another. In this article we describe an evaluation method that recruits the already-pervasive medium of Web forums to support collection and discussion of user critical incidents. We describe a Web forum tool created to support this discussion, the Collaborative Critical Incident Tool (CCIT). The notion of “critical incident” is adapted from Flanagan (1956), who debriefed test pilots in order to gather and analyze episodes in which something went surprisingly good or bad. Flanagan’s method has become a mainstay of human factors evaluation (Meister, 1985). In our method, users can post a critical incident report to the forum at any time. Subsequently, other users, as well as evaluators and system developers, can post threaded replies. This improves the critical incident method by permitting follow-up questions and other conversational elaboration and refinement of original reports.

scholarly journals Content Noise Detection Model Using Deep Learning in Web Forums

2020 ◽  
Vol 12 (12) ◽  
pp. 5074
Author(s):  
Jiyoung Woo ◽  
Jaeseok Yun
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Model ◽  
Detection Model ◽  
Proposed Model ◽  
Web Forum ◽  
Web Forums ◽  
Conventional Machine ◽  
Text Features ◽  
Deep Learning Model

Spam posts in web forum discussions cause user inconvenience and lower the value of the web forum as an open source of user opinion. In this regard, as the importance of a web post is evaluated in terms of the number of involved authors, noise distorts the analysis results by adding unnecessary data to the opinion analysis. Here, in this work, an automatic detection model for spam posts in web forums using both conventional machine learning and deep learning is proposed. To automatically differentiate between normal posts and spam, evaluators were asked to recognize spam posts in advance. To construct the machine learning-based model, text features from posted content using text mining techniques from the perspective of linguistics were extracted, and supervised learning was performed to distinguish content noise from normal posts. For the deep learning model, raw text including and excluding special characters was utilized. A comparison analysis on deep neural networks using the two different recurrent neural network (RNN) models of the simple RNN and long short-term memory (LSTM) network was also performed. Furthermore, the proposed model was applied to two web forums. The experimental results indicate that the deep learning model affords significant improvements over the accuracy of conventional machine learning associated with text features. The accuracy of the proposed model using LSTM reaches 98.56%, and the precision and recall of the noise class reach 99% and 99.53%, respectively.

Sign in / Sign up

Export Citation Format

Share Document