The idea that text in a particular field of discourse is organized into lexical patterns, which can be visualized as networks of words that collocate with each other, was originally proposed by Phillips (1983). This idea has important theoretical implications for our understanding of the relationship between the lexis and the text and (ultimately) between the text and the discourse community/ the mind of the speaker. Although the approaches to date have offered different possibilities for constructing collocation networks, we argue that they have not yet successfully operationalized some of the desired features of such networks. In this study, we revisit the concept of collocation networks and introduce GraphColl, a new tool developed by the authors that builds collocation networks from user-defined corpora. In a case study using data from McEnery's (2006a) study of the Society for the Reformation of Manners Corpus (SRMC), we demonstrate that collocation networks provide important insights into meaning relationships in language.

A significant milestone in the development of physicallydynamic surfaces is the ability for buttons to protrude outwards from any location on a touch-screen. As a first step toward developing interaction requirements for this technology we conducted a survey of 1515 electronic push buttons in everyday home environments. We report a characterisation that describes the features of the data set and discusses important button properties that we expect will inform the design of future physically-dynamic devices and surfaces.

Identity resolution capability for social networking profiles is important for a range of purposes, from open-source intelligence applications to forming semantic web connections. Yet replication of research in this area is hampered by the lack of access to ground-truth data linking the identities of profiles from different networks. Almost all data sources previously used by researchers are no longer available, and historic datasets are both of decreasing relevance to the modern social networking landscape and ethically troublesome regarding the preservation and publication of personal data. We present and evaluate a method which provides researchers in identity resolution with easy access to a realistically-challenging labelled dataset of online profiles, drawing on four of the currently largest and most influential online social networks. We validate the comparability of samples drawn through this method and discuss the implications of this mechanism for researchers as well as potential alternatives and extensions.

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