Web-Based Visualization of Media Data for Driver Assistance

2010 ◽  
Author(s):  
Bin Wang ◽  
Sathiamoorthy Manoharan ◽  
Reinhard Klette
Keyword(s):  
Driver Assistance ◽  
Web Based ◽  
Media Data

Analyzing Social Networks to Mine Important Friends

2013 ◽  
pp. 90-104 ◽  
Author(s):  
Carson K.-S. Leung ◽  
Irish J. M. Medina ◽  
Syed K. Tanbeer
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Social Networking Sites ◽  
Web Based ◽  
The Social ◽  
Social Entity ◽  
Social Entities ◽  
Media Data ◽  
Media Mining

The emergence of Web-based communities and social networking sites has led to a vast volume of social media data, embedded in which are rich sets of meaningful knowledge about the social networks. Social media mining and social network analysis help to find a systematic method or process for examining social networks and for identifying, extracting, representing, and exploiting meaningful knowledge—such as interdependency relationships among social entities in the networks—from the social media. This chapter presents a system for analyzing the social networks to mine important groups of friends in the networks. Such a system uses a tree-based mining approach to discover important friend groups of each social entity and to discover friend groups that are important to social entities in the entire social network.

The Language Archive and its solutions for sustainable endangered languages corpora

Book 2 0 ◽  
2014 ◽  
Vol 4 (1) ◽  
pp. 5-20
Author(s):  
Sebastian Drude ◽  
Daan Broeder ◽  
Paul Trilsbeek
Keyword(s):  
Digital Data ◽  
Endangered Languages ◽  
Max Planck ◽  
Web Based ◽  
Data Archives ◽  
Long Time ◽  
Time Availability ◽  
Ongoing Development ◽  
Multi Media ◽  
Media Data

Since the late 1990s, the technical group at the Max-Planck-Institute for Psycholinguistics has worked on solutions for important challenges in building sustainable data archives, in particular, how to guarantee long-time-availability of digital research data for future research.The support for the well-known DOBES (Documentation of Endangered Languages) programme has greatly inspired and advanced this work, and lead to the ongoing development of a whole suite of tools for annotating, cataloguing and archiving multi-media data. At the core of the LAT (Language Archiving Technology) tools is the IMDI metadata schema, now being integrated into a larger network of digital resources in the European CLARIN project. The multi-media annotator ELAN (with its web-based cousin ANNEX) is now well known not only among documentary linguists.We aim at presenting an overview of the solutions, both achieved and in development, for creating and exploiting sustainable digital data, in particular in the area of documenting languages and cultures, and their interfaces with related other developments.

scholarly journals Measuring Semantic Relatedness between Flickr Images: From a Social Tag Based View

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Zheng Xu ◽  
Xiangfeng Luo ◽  
Yunhuai Liu ◽  
Lin Mei ◽  
Chuanping Hu
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Information Theory ◽  
Semantic Relatedness ◽  
Data Sets ◽  
Order Information ◽  
Web Based ◽  
Social Tag ◽  
Media Data ◽  
Flickr Images

Relatedness measurement between multimedia such as images and videos plays an important role in computer vision, which is a base for many multimedia related applications including clustering, searching, recommendation, and annotation. Recently, with the explosion of social media, users can upload media data and annotate content with descriptive tags. In this paper, we aim at measuring the semantic relatedness of Flickr images. Firstly, four information theory based functions are used to measure the semantic relatedness of tags. Secondly, the integration of tags pair based on bipartite graph is proposed to remove the noise and redundancy. Thirdly, the order information of tags is added to measure the semantic relatedness, which emphasizes the tags with high positions. The data sets including 1000 images from Flickr are used to evaluate the proposed method. Two data mining tasks including clustering and searching are performed by the proposed method, which shows the effectiveness and robustness of the proposed method. Moreover, some applications such as searching and faceted exploration are introduced using the proposed method, which shows that the proposed method has broad prospects on web based tasks.

scholarly journals Distant Supervision for Mental Health Management in Social Media: Suicide Risk Classification System Development Study

10.2196/26119 ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. e26119
Author(s):  
Guanghui Fu ◽  
Changwei Song ◽  
Jianqiang Li ◽  
Yue Ma ◽  
Pan Chen ◽  
...  
Keyword(s):  
Social Media ◽  
Suicide Risk ◽  
Health Management ◽  
System Development ◽  
Common People ◽  
Web Based ◽  
Social Media Data ◽  
Distant Supervision ◽  
Data Source ◽  
Media Data

Background Web-based social media provides common people with a platform to express their emotions conveniently and anonymously. There have been nearly 2 million messages in a particular Chinese social media data source, and several thousands more are generated each day. Therefore, it has become impossible to analyze these messages manually. However, these messages have been identified as an important data source for the prevention of suicide related to depression disorder. Objective We proposed in this paper a distant supervision approach to developing a system that can automatically identify textual comments that are indicative of a high suicide risk. Methods To avoid expensive manual data annotations, we used a knowledge graph method to produce approximate annotations for distant supervision, which provided a basis for a deep learning architecture that was built and refined by interactions with psychology experts. There were three annotation levels, as follows: free annotations (zero cost), easy annotations (by psychology students), and hard annotations (by psychology experts). Results Our system was evaluated accordingly and showed that its performance at each level was promising. By combining our system with several important psychology features from user blogs, we obtained a precision of 80.75%, a recall of 75.41%, and an F1 score of 77.98% for the hardest test data. Conclusions In this paper, we proposed a distant supervision approach to develop an automatic system that can classify high and low suicide risk based on social media comments. The model can therefore provide volunteers with early warnings to prevent social media users from committing suicide.

From Reactive to Proactive Use of Social Media in Emergency Response

2016 ◽  
pp. 236-252 ◽  
Author(s):  
Kees Boersma ◽  
Dominique Diks ◽  
Julie Ferguson ◽  
Jeroen Wolbers
Keyword(s):  
Social Media ◽  
Emergency Response ◽  
Learning Strategy ◽  
Pilot Project ◽  
Project Planning ◽  
Web Based ◽  
New Information ◽  
Use Of Social Media ◽  
The One ◽  
Media Data

This chapter examines the introduction and implementation of the pilot project Twitcident in an emergency response room setting. Twitcident is a web-based system for filtering, searching and analyzing data on real-world incidents or crises. Social media data is seen as important for emergency response operations: it can be used as an ‘early warning monitoring system' to detect social unrest, and for improving common operational pictures (COPs). This chapter shows that the expectations on the functioning of the tool were not fully met: first it was hard for the response room professionals to make sense of the data and second, the management did not develop a proper project planning. The recommendations are twofold. On the one hand, the professionals who work with Twitcident must invest in developing new information management routines. On the other hand, the response room management needs to create a much more inclusive project learning strategy.

scholarly journals From Reactive to Proactive Use of Social Media in Emergency Response

2019 ◽  
pp. 602-618 ◽  
Author(s):  
Kees Boersma ◽  
Dominique Diks ◽  
Julie Ferguson ◽  
Jeroen Wolbers
Keyword(s):  
Social Media ◽  
Emergency Response ◽  
Learning Strategy ◽  
Pilot Project ◽  
Project Planning ◽  
Web Based ◽  
New Information ◽  
Use Of Social Media ◽  
The One ◽  
Media Data

This chapter examines the introduction and implementation of the pilot project Twitcident in an emergency response room setting. Twitcident is a web-based system for filtering, searching and analyzing data on real-world incidents or crises. Social media data is seen as important for emergency response operations: it can be used as an ‘early warning monitoring system' to detect social unrest, and for improving common operational pictures (COPs). This chapter shows that the expectations on the functioning of the tool were not fully met: first it was hard for the response room professionals to make sense of the data and second, the management did not develop a proper project planning. The recommendations are twofold. On the one hand, the professionals who work with Twitcident must invest in developing new information management routines. On the other hand, the response room management needs to create a much more inclusive project learning strategy.

Distant Supervision for Mental Health Management in Social Media: Suicide Risk Classification System Development Study (Preprint)

2020 ◽  
Author(s):  
Guanghui Fu ◽  
Changwei Song ◽  
Jianqiang Li ◽  
Yue Ma ◽  
Pan Chen ◽  
...  
Keyword(s):  
Social Media ◽  
Suicide Risk ◽  
Health Management ◽  
System Development ◽  
Common People ◽  
Web Based ◽  
Social Media Data ◽  
Distant Supervision ◽  
Data Source ◽  
Media Data

BACKGROUND Web-based social media provides common people with a platform to express their emotions conveniently and anonymously. There have been nearly 2 million messages in a particular Chinese social media data source, and several thousands more are generated each day. Therefore, it has become impossible to analyze these messages manually. However, these messages have been identified as an important data source for the prevention of suicide related to depression disorder. OBJECTIVE We proposed in this paper a distant supervision approach to developing a system that can automatically identify textual comments that are indicative of a high suicide risk. METHODS To avoid expensive manual data annotations, we used a knowledge graph method to produce approximate annotations for distant supervision, which provided a basis for a deep learning architecture that was built and refined by interactions with psychology experts. There were three annotation levels, as follows: free annotations (zero cost), easy annotations (by psychology students), and hard annotations (by psychology experts). RESULTS Our system was evaluated accordingly and showed that its performance at each level was promising. By combining our system with several important psychology features from user blogs, we obtained a precision of 80.75%, a recall of 75.41%, and an F1 score of 77.98% for the hardest test data. CONCLUSIONS In this paper, we proposed a distant supervision approach to develop an automatic system that can classify high and low suicide risk based on social media comments. The model can therefore provide volunteers with early warnings to prevent social media users from committing suicide.

The Effectiveness of Big Data in Social Networks

2015 ◽  
pp. 362-381
Author(s):  
Khine Khine Nyunt ◽  
Noor Zaman
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Big Data ◽  
Social Networking ◽  
User Generated Content ◽  
Web Based ◽  
The Social ◽  
Internet Application ◽  
Media Data ◽  
Media Mining

In this chapter, we will discuss how “big data” is effective in “Social Networks” which will bring huge opportunities but difficulties though challenges yet ahead to the communities. Firstly, Social Media is a strategy for broadcasting, while Social Networking is a tool and a utility for connecting with others. For this perspective, we will introduce the characteristic and fundamental models of social networks and discuss the existing security & privacy for the user awareness of social networks in part I. Secondly, the technological built web based internet application of social media with Web2.0 application have transformed users to allow creation and exchange of user-generated content which play a role in big data of unstructured contents as well as structured contents. Subsequently, we will introduce the characteristic and landscaping of the big data in part II. Finally, we will discuss the algorithms for marketing and social media mining which play a role how big data fit into the social media data.

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