• Automated Web issue analysis: A nurse prescribing case study

      Thelwall, Mike; Thelwall, Saheeda; Fairclough, Ruth (Elsevier, 2006)
      Web issue analysis, a new automated technique designed to rapidly give timely management intelligence about a topic from an automated large-scale analysis of relevant pages from the Web, is introduced and demonstrated. The technique includes hyperlink and URL analysis to identify common direct and indirect sources of Web information. In addition, text analysis through natural language processing techniques is used identify relevant common nouns and noun phrases. A case study approach is taken, applying Web issue analysis to the topic of nurse prescribing. The results are presented in descriptive form and a qualitative analysis is used to argue that new information has been found. The nurse prescribing results demonstrate interesting new findings, such as the parochial nature of the topic in the UK, an apparent absence of similar concepts internationally, at least in the English-speaking world, and a significant concern with mental health issues. These demonstrate that automated Web issue analysis is capable of quickly delivering new insights into a problem. General limitations are that the success of Web issue analysis is dependant upon the particular topic chosen and the ability to find a phrase that accurately captures the topic and is not used in other contexts, as well as being language-specific.
    • Detecting high-functioning autism in adults using eye tracking and machine learning

      Yaneva, Victoria; Ha, Le An; Eraslan, Sukru; Yesilada, Yeliz; Mitkov, Ruslan (Institute of Electrical and Electronics Engineers (IEEE), 2020-04-30)
      The purpose of this study is to test whether visual processing differences between adults with and without highfunctioning autism captured through eye tracking can be used to detect autism. We record the eye movements of adult participants with and without autism while they look for information within web pages. We then use the recorded eye-tracking data to train machine learning classifiers to detect the condition. The data was collected as part of two separate studies involving a total of 71 unique participants (31 with autism and 40 control), which enabled the evaluation of the approach on two separate groups of participants, using different stimuli and tasks. We explore the effects of a number of gaze-based and other variables, showing that autism can be detected automatically with around 74% accuracy. These results confirm that eye-tracking data can be used for the automatic detection of high-functioning autism in adults and that visual processing differences between the two groups exist when processing web pages.