Mining large-scale smartphone data for personality studies

Chittaranjan, Gokul; Blom, Jan; Gática-Pérez, Daniel

doi:10.1007/s00779-011-0490-1

Cited by 342 publications

(307 citation statements)

References 21 publications

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“…In more recent years, the interest in personality recognition has grown in two areas: the analysis of human behaviour and social network analysis. Several studies have started exploring the wealth of behavioral data made available by cameras, microphones [33], wearable sensors [22], and mobile phones [11] linking personality traits to dimensions such as face to face interaction, speech video and text transcriptions. From the other hand, researchers have also focused on personality prediction from corpora of social network data, like Twitter and Facebook, exploiting either linguistic features in status updates, social features such as friends count, and daily activity [51,9].…”

Section: Related Workmentioning

confidence: 99%

Overview of the PAN/CLEF 2015 Evaluation Lab

Stamatatos

Potthast

Rangel

et al. 2015

Lecture Notes in Computer Science

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Stamatatos, E.; Potthast, M.; Rangel, F.; Rosso, P.; . Abstract This paper presents an overview of the PAN/CLEF evaluation lab. During the last decade, PAN has been established as the main forum of text mining research focusing on the identification of personal traits of authors left behind in texts unintentionally. PAN 2015 comprises three tasks: plagiarism detection, author identification and author profiling studying important variations of these problems. In plagiarism detection, community-driven corpus construction is introduced as a new way of developing evaluation resources with diversity. In author identification, cross-topic and cross-genre author verification (where the texts of known and unknown authorship do not match in topic and/or genre) is introduced. A new corpus was built for this challenging, yet realistic, task covering four languages. In author profiling, in addition to usual author demographics, such as gender and age, five personality traits are introduced (openness, conscientiousness, extraversion, agreeableness, and neuroticism) and a new corpus of Twitter messages covering four languages was developed. In total, 53 teams participated in all three tasks of PAN 2015 and, following the practice of previous editions, software submissions were required and evaluated within the TIRA experimentation framework.

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Section: Related Workmentioning

confidence: 99%

Overview of the PAN/CLEF 2015 Evaluation Lab

Stamatatos

Potthast

Rangel

et al. 2015

Lecture Notes in Computer Science

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“…Namely, we aim to show that there are noticeable differences between users of dissimilar personalities in terms of their response to UI design manipulations. Given that prior studies have showed the feasibility of automatically constructing users' personality profiles based on their online behavior (Chittaranjan et al, 2012;Golbeck et al, 2011), our proof-of-concept employs a simple survey-based method for measuring users' personality.…”

Section: Personalityzation: Grounding Ui Personalization In the Psychmentioning

confidence: 99%

Untitled

2015

THCI

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Abstract:Personalization is an effective means for accommodating differences between individuals. Therefore, the personalization of a system's user interface (UI) features can enhance usability. To date, UI personalization approaches have been largely divorced from psychological theories of personality, and the user profiles constructed by extant personalization techniques do not map directly onto the fundamental personality traits examined in the psychology literature. In line with recent calls to ground the design of information systems in behavioral theory, we maintain that personalization that is informed by psychology literature is advantageous. More specifically, we advocate an approach termed "personalityzation", where UI features are adapted to an explicit model of a user's personality. We demonstrate the proposed personalityzation approach through a proof-of-concept in the context of social recommender systems. We identify two key contributions to information systems research. First, extending prior works on adaptive interfaces, we introduce a UI personalization framework that is grounded in psychology theory of personality. Second, we reflect on how our proposed personalityzation framework could inform the discourse in design research regarding the theoretical grounding of system's design.

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“…Behavior analysis [6], analyze the behavior of a mobile user by considering various activities carried out by the user on the mobile phone and its social impact. Behavior analysis makes the mobile phone tailored for a person based on the interaction made by the person.…”

Section: Behavior Analysismentioning

confidence: 99%

An Overview on Mobile Data Mining

Natarajasivan¹,

Govindarajan²

2014

IJCA

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In early days the mobile phones are considered to perform only telecommunication operation. This scenario of mobile phones as communication devices changed with the emergence of a new class of mobile devices called the smart phones. These smart phones in addition to being used as a communication device are capable of doing things that a computer does. In recent times the smart phone are becoming more and more powerful in both computing and storage aspects. The data generated by the smart phone provide a means to get new knowledge about various aspects like usage, movement of the user etc. This paper provides an introduction to Mobile Data Mining and its types.

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Mining large-scale smartphone data for personality studies

Cited by 342 publications

References 21 publications

Overview of the PAN/CLEF 2015 Evaluation Lab

Overview of the PAN/CLEF 2015 Evaluation Lab

Untitled

An Overview on Mobile Data Mining

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