TIE: Temporal Interaction Explorer for Co-presence Communities

Boston, Daniel; Borcea, Cristian

doi:10.1109/dasc.2011.145

“…We also wanted to investigate GDC, D-GDC and K-Clique's absolute performance against the ground truth. We constructed the ground truth of our data set through a careful timeline survey using a tool of our own construction called TIE, the Temporal Interaction Explorer [14].…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…• it evaluates GDC and DGDC against a careful reconstruction of "true groups" created using a novel tool (TIE [14]) and deep investigation of the source data, a reconstruction named the ground truth data set (GTS). We show that GDC and DGDC outperform K-Clique in both accuracy and terseness.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Bluetooth co-location traces in group discovery algorithms

Boston¹,

Mardenfeld²,

Pan³

et al. 2014

Pervasive and Mobile Computing

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Smart phones can collect and share Bluetooth co-location traces to identify ad hoc or semi-permanent social groups, which can enhance recommender systems or allow detection of epidemic events. Group discovery using Bluetooth co-location is practical due to low power consumption, short range, and applicability to decentralization. This paper presents the Group Discovery using Co-location traces (GDC) and Decentralized GDC (DGDC) algorithms, which leverage user meeting frequency and duration to accurately detect groups. GDC and DGDC are validated on one month of data collected from 141 smart phones carried by students on our campus, and by comparison against ground-truth groups.

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“…Belingerio et al (2010) use three-dimensional graphs combined with hierarchical trees to identify eras in social networks. Boston and Borcea (2011) posit TIE, a visualisation tool for analysis of detected communities from data collected through smartphone Bluetooth devices. propose Matrixify, an approach that combines temporal and static network visualisations to analyse social network change over time in historical data sets.…”

Section: Social Network and Social Media Analysismentioning

confidence: 99%

A Framework for the Forensic Analysis of User Interaction with Social Media

Haggerty

¹

,

Casson

²

,

Haggerty

³

et al. 2012

International Journal of Digital Crime and Forensics

2

0

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The increasing use of social media, applications or platforms that allow users to interact online, ensures that this environment will provide a useful source of evidence for the forensics examiner. Current tools for the examination of digital evidence find this data problematic as they are not designed for the collection and analysis of online data. Therefore, this paper presents a framework for the forensic analysis of user interaction with social media. In particular, it presents an inter-disciplinary approach for the quantitative analysis of user engagement to identify relational and temporal dimensions of evidence relevant to an investigation. This framework enables the analysis of large data sets from which a (much smaller) group of individuals of interest can be identified. In this way, it may be used to support the identification of individuals who might be ‘instigators’ of a criminal event orchestrated via social media, or a means of potentially identifying those who might be involved in the ‘peaks’ of activity. In order to demonstrate the applicability of the framework, this paper applies it to a case study of actors posting to a social media Web site.

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A Framework for the Forensic Analysis of User Interaction with Social Media

Haggerty

¹

,

Casson

²

,

Haggerty

³

et al.

Emerging Digital Forensics Applications for Crime Detection, Prevention, and Security

View full text Add to dashboard Cite

The increasing use of social media, applications or platforms that allow users to interact online, ensures that this environment will provide a useful source of evidence for the forensics examiner. Current tools for the examination of digital evidence find this data problematic as they are not designed for the collection and analysis of online data. Therefore, this paper presents a framework for the forensic analysis of user interaction with social media. In particular, it presents an inter-disciplinary approach for the quantitative analysis of user engagement to identify relational and temporal dimensions of evidence relevant to an investigation. This framework enables the analysis of large data sets from which a (much smaller) group of individuals of interest can be identified. In this way, it may be used to support the identification of individuals who might be ‘instigators’ of a criminal event orchestrated via social media, or a means of potentially identifying those who might be involved in the ‘peaks’ of activity. In order to demonstrate the applicability of the framework, this paper applies it to a case study of actors posting to a social media Web site.

show abstract

TIE: Temporal Interaction Explorer for Co-presence Communities

Cited by 3 publications

References 22 publications

Leveraging Bluetooth co-location traces in group discovery algorithms

Leveraging Bluetooth co-location traces in group discovery algorithms

A Framework for the Forensic Analysis of User Interaction with Social Media

A Framework for the Forensic Analysis of User Interaction with Social Media

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