Delivering Social Multimedia Content with Scalability

2016 ◽  
pp. 383-399 ◽  
Author(s):  
Irene Kilanioti ◽  
George A. Papadopoulos
Keyword(s):  
Multimedia Content ◽  
Social Multimedia

Triangulating Social Multimedia Content for Event Localization using Flickr and Twitter

2014 ◽  
Vol 19 (5) ◽  
pp. 694-715 ◽  
Author(s):  
George Panteras ◽  
Sarah Wise ◽  
Xu Lu ◽  
Arie Croitoru ◽  
Andrew Crooks ◽  
...  
Keyword(s):  
Multimedia Content ◽  
Social Multimedia ◽  
Event Localization

Using Social Multimedia Content to Inform Emergency Planning of Recurring and Cyclical Events in Local Communities

2015 ◽  
Vol 12 (3) ◽  
Author(s):  
Patrick C. Shih ◽  
Kyungsik Han ◽  
John M. Carroll
Keyword(s):  
Social Media ◽  
Local Community ◽  
Community Context ◽  
Emergency Planning ◽  
Mitigation Strategies ◽  
Planning System ◽  
Multimedia Content ◽  
Social Multimedia ◽  
Local Municipalities ◽  
Response And Recovery

AbstractSocial media has been widely adopted for assisting the efforts in emergency response and recovery, but it has been underutilized for emergency planning purposes. Emergency planning in a local community context must leverage accessible and free resources such as social media, because it is largely a volunteer enterprise. We describe our fieldwork with local annual festival emergency planning teams that led to the design of the Community Incident Report (CIR). CIR is a novel emergency planning system that externalizes community knowledge on persisting issues and common mitigation strategies by integrating police reports, local crisis information, and social multimedia content to foster citizens’ awareness of local emergency information and to assist emergency planners in planning for recurring and cyclical events. We provide a use case analysis of CIR and its evaluation with 20 local residents, and discuss how it could be extended to inform emergency planning for other community events and local municipalities that share similar characteristics.

Semantic tag recommendation based on associated words exploiting the interwiki links of Wikipedia

2017 ◽  
Vol 44 (3) ◽  
pp. 298-313 ◽  
Author(s):  
Hyun-Ki Hong ◽  
Gun-Woo Kim ◽  
Dong-Ho Lee
Keyword(s):  
Multimedia Content ◽  
Ranking Algorithm ◽  
Digital Cameras ◽  
Social Image ◽  
Tag Recommendation ◽  
Image Sharing ◽  
Social Multimedia ◽  
Similar Images ◽  
Smart Mobile

The volumes of multimedia content and users have increased on social multimedia sites due to the prevalence of smart mobile devices and digital cameras. It is common for users to take pictures and upload them to image-sharing websites using their smartphones. However, the tag characteristics deteriorate the quality of tag-based image retrieval and decrease the reliability of social multimedia sites. In this article, we propose a semantic tag recommendation technique exploiting associated words that are semantically similar or related to each other using the interwiki links of Wikipedia. First, we generate a word relationship graph after extracting meaningful words from each article in Wikipedia. The candidate words are then rearranged according to importance by applying a link-based ranking algorithm and then the top-k words are defined as the associated words for the article. When a user uploads an image, we collect visually similar images from a social image database. After propagating the proper tags from the collected images, we recommend associated words related to the candidate tags. Our experimental results show that the proposed method can improve the accuracy by up to 14% compared with other works and that exploiting associated words makes it possible to perform semantic tag recommendation.

Social Multimedia Mining

2011 ◽  
pp. 1-16
Author(s):  
Georgios Lappas
Keyword(s):  
Public Relations ◽  
Online Social Networks ◽  
Web Mining ◽  
Research Area ◽  
Multimedia Content ◽  
Social Multimedia ◽  
Research Areas ◽  
Media Research ◽  
New Challenges ◽  
Social Media Research

In recent years there is a vast and rapidly growing amount of multimedia content available online. Web 2.0 and online social networks have dramatically influenced the growing amount of multimedia content due to the fact that users become more active producers and distributors of such multimedia context. This work conceptualizes and introduces the concept of social multimedia mining as a new emerging research area that combines web mining research, multimedia research and social media research. New challenges in multimedia research, social network analysis research as well as trends and opportunities in research areas of social and communication studies and more specific in politics, public relations, public administration, marketing and advertising are discussed in this chapter.

Cloud-Based Multimedia Content Protection System

2020 ◽  
pp. 164-169
Author(s):  
B. Aparna ◽  
S. Madhavi ◽  
G. Mounika ◽  
P. Avinash ◽  
S. Chakravarthi
Keyword(s):  
Large Scale ◽  
Protection System ◽  
Multimedia Content ◽  
Computationally Efficient ◽  
Content Protection ◽  
Protection Systems ◽  
Multimedia Content Protection ◽  
High Scalability ◽  
Cloud Infrastructures ◽  
Rapid Deployment

We propose a new design for large-scale multimedia content protection systems. Our design leverages cloud infrastructures to provide cost efficiency, rapid deployment, scalability, and elasticity to accommodate varying workloads. The proposed system can be used to protect different multimedia content types, including videos, images, audio clips, songs, and music clips. The system can be deployed on private and/or public clouds. Our system has two novel components: (i) method to create signatures of videos, and (ii) distributed matching engine for multimedia objects. The signature method creates robust and representative signatures of videos that capture the depth signals in these videos and it is computationally efficient to compute and compare as well as it requires small storage. The distributed matching engine achieves high scalability and it is designed to support different multimedia objects. We implemented the proposed system and deployed it on two clouds: Amazon cloud and our private cloud. Our experiments with more than 11,000 videos and 1 million images show the high accuracy and scalability of the proposed system. In addition, we compared our system to the protection system used by YouTube and our results show that the YouTube protection system fails to detect most copies of videos, while our system detects more than 98% of them.

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