## 1 Introduction  
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. Fromthe perspective of web archiving and digital preservation, taking these modalities into account is very important. As we move towards more intelligent archiving and more intelligent post-crawl analysis of the data hidden within digital archives, the ability to leverage the vast array of different data modalities becomes even more crucial.  
Adaptive web crawlers, which target specific keywords, are now becoming more commonplace. These crawlers make fast, near-real time, decisions about what (textual) content should be crawled based on the presence or absence of keywords given at the start of the crawl. 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]. In the ARCOMEM crawlers, ETOEs are primarily detected using state-of-the-art text analysis techniques, although, as we will see later in the paper, ETOEs can also be detected in multimedia content. 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). Once a crawl is completed (or potentially, whilst it is still running), a second set of more-complex processing is applied to the crawled documents to select a subset for export, whilst, at the same time, producing vast amounts of metadata and analysis results, which can be exported alongside it. A more complete overview of the ARCOMEM system is given in Section 3.  
This paper builds upon our work presented at the 1 st International Workshop on Archiving Community Memories [3], where we discussed many of facets of multimedia analysis with respect to web archiving. Within this paper, we discuss some of the needs and opportunities for both analysing the multimedia data on the web and within an archive of community memories. Practical embodiments of the discussed techniques developed within the ARCOMEM project are detailed, with full references to our published works in which these techniques are evaluated. We also discuss how multimedia data might be exploited during the initial creation of the archive, given sufficient computational resources to analyse the data at a high enough rate.  
The structure of this paper is as follows: Section 2 gives an overview of some key recent trends, approaches and techniques in multimedia analysis. Section 3 describes the approach to web archiving and automated archive analysis developed in the ARCOMEM project. Section 4 describes a number of use cases for applying multimedia analysis to web and social web data, that can either be used for post-crawl analysis or intelligent document appraisal within a web crawl. Finally, we conclude with some remarks about the future outlook of our research on multimedia analysis with respect to web crawling and web analytics.