I know where you live : Inferring details of people's lives by visualizing publicly shared location data
- Submitting institution
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King's College London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 103537497
- Type
- E - Conference contribution
- DOI
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10.1145/2858036.2858272
- Title of conference / published proceedings
- Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI)
- First page
- 1
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- May
- Year of publication
- 2016
- URL
-
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- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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2
- Research group(s)
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-
- Citation count
- 13
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper presents an empirical study that measures human performance in determining the geolocation types (i.e., home, work, leisure, and transport) of tweet data using different data representations, discovering that visualization can improve human performance noticeably. The study demonstrated that the participants can easily identify a specific location through a simple tabular representation. The study involved UK participants and used USA geo-location data to minimize the impact of local knowledge, while using ANOVA for validation. This paper received an honorable mention in CHI2016 (top 5% of accepted papers) and was reported in MIT News (https://news.mit.edu/2016/twitterlocation- data-homes-workplaces-0517) for evidencing privacy risks.
- Author contribution statement
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- Non-English
- No
- English abstract
- -