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We propose a methodological approach to analyze the content of hyperlink networks which represent networked public spheres on the Internet. Using the case of the food safety movement in the United States, we demonstrate how to generate a hyperlink network with the web crawling tool Issue Crawler and merge it with the results of a probabilistic topic model of the network’s content. Combining hyperlink networks and content analysis allows us to interpret such a network in its entirety and with regard to the mobilizing potentials of specific sub-issues of the movement. We focus on two specific sub-issues in the food safety network, genetically modified food and food control, in order to trace the involved websites and their interlinking structures, respectively. [ABSTRACT FROM AUTHOR]

In this article, we focus on noise in the sense of irrelevant information in a data set as a specific methodological challenge of web research in the era of big data. We empirically evaluate several methods for filtering hyperlink networks in order to reconstruct networks that contain only webpages that deal with a particular issue. The test corpus of webpages was collected from hyperlink networks on the issue of food safety in the United States and Germany. We applied three filtering strategies and evaluated their performance to exclude irrelevant content from the networks: keyword filtering, automated document classification with a machine-learning algorithm, and extraction of core networks with network-analytical measures. Keyword filtering and automated classification of webpages were the most effective methods for reducing noise, whereas extracting a core network did not yield satisfying results for this case. [ABSTRACT FROM AUTHOR]