Organs of the human brain, created by the human hand? The social epistemology of information technology

Purpose
– Information science has been conceptualized as a partly unreflexive response to developments in information and computer technology, and, most powerfully, as part of the gestalt of the computer. The computer was viewed as an historical accident in the original formulation of the gestalt. An alternative, and timely, approach to understanding, and then dissolving, the gestalt would be to address the motivating technology directly, fully recognizing it as a radical human construction. This paper aims to address the issues.

Design/methodology/approach
– The paper adopts a social epistemological perspective and is concerned with collective, rather than primarily individual, ways of knowing.

Findings
– Information technology tends to be received as objectively given, autonomously developing, and causing but not itself caused, by the language of discussions in information science. It has also been characterized as artificial, in the sense of unnatural, and sometimes as threatening. Attitudes to technology are implied, rather than explicit, and can appear weak when articulated, corresponding to collective repression.

Research limitations/implications
– Receiving technology as objectively given has an analogy with the Platonist view of mathematical propositions as discovered, in its exclusion of human activity, opening up the possibility of a comparable critique which insists on human agency.

Originality/value
– Apprehensions of information technology have been raised to consciousness, exposing their limitations.

Cyber Security Research: a vision for the UK: Finance challenges

Invited talk, representing the Royal Society

Twitter Sentiment Analysis for Security-Related Information Gathering

Analysing public sentiment about future events, such as demonstration or parades, may provide valuable information while estimating the level of disruption and disorder during these events. Social media, such as Twitter or Facebook, provides views and opinions of users related to any public topics. Consequently, sentiment analysis of social media content may be of interest to different public sector organisations, especially in the security and law enforcement sector. In this paper we present a lexicon-based approach to sentiment analysis of Twitter content. The algorithm performs normalisation of the sentiment in an effort to provide intensity of the sentiment rather than positive/negative label. Following this, we evaluate an evidence-based combining function that supports the classification process in cases when positive and negative words co-occur in a tweet. Finally, we illustrate a case study examining the relation between sentiment of twitter posts related to English Defence League and the level of disorder during the EDL related events.