Abstract
The data contained on the web and the social web are inherently multimedia and consist of a mixture of textual, visual and audio modalities. Community memories embodied on the web and social web contain a rich mixture of data from these modalities. In many ways, the web is the greatest resource ever created by human-kind. However, due to the dynamic and distributed nature of the web, its content changes, appears and disappears on a daily basis. Web archiving provides a way of capturing snapshots of (parts of) the web for preservation and future analysis. This paper provides an overview of techniques we have developed within the context of the EU funded ARCOMEM (ARchiving COmmunity MEMories) project to allow multimedia web content to be leveraged during the archival process and for post-archival analysis. Through a set of use cases, we explore several practical applications of multimedia analytics within the realm of web archiving, web archive analysis and multimedia data on the web in general.
Highlights
Community memories embodied on the web and within social media are inherently multimedia in nature and contain data and information in many different auditory, visual and textual forms
Multimedia analysis is typically very computationally expensive, especially compared to simple text analysis. This presents many challenges when working with multimedia web archives, especially if we want to integrate multimedia analysis into the decision making that happens inside an adaptive crawl
To deal with the problem of computational complexity in the ARCOMEM crawlers, the real-time decision making required for dynamically controlling the crawl is performed by fast text analysis techniques, which determine whether the outlinks of a document should be followed based on the relevance of the document to a crawl specification
Summary
Community memories embodied on the web and within social media are inherently multimedia in nature and contain data and information in many different auditory, visual and textual forms. Multimedia analysis is typically very computationally expensive, especially compared to simple text analysis This presents many challenges when working with multimedia web archives, especially if we want to integrate multimedia analysis into the decision making that happens inside an adaptive crawl. Within the EU funded ARCOMEM (ARchiving COmmunity MEMories) project [1], we have developed adaptive web and social-web crawlers that produce highly focussed web archives relating to specific entities (people, places, and organisations), topics, opinions and events (ETOEs) [2]. To deal with the problem of computational complexity in the ARCOMEM crawlers, the real-time decision making required for dynamically controlling the crawl is performed by fast text analysis techniques, which determine whether the outlinks of a document should be followed based on the relevance of the document to a crawl specification (itself consisting of sets of ETOEs). We conclude with some remarks about the future outlook of our research on multimedia analysis with respect to web crawling and web analytics
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