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			<subfield code="a">CONF</subfield>
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			<subfield code="a">Chittaranjan_ISWC11_2011/IDIAP</subfield>
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		<datafield tag="245" ind1=" " ind2=" ">
			<subfield code="a">Who's Who with Big-Five: Analyzing and Classifying Personality Traits with Smartphones</subfield>
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		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Chittaranjan, Gokul</subfield>
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		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Blom, Jan</subfield>
		</datafield>
		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Gatica-Perez, Daniel</subfield>
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		<datafield tag="856" ind1="4" ind2="0">
			<subfield code="i">EXTERNAL</subfield>
			<subfield code="u">http://publications.idiap.ch/attachments/papers/2011/Chittaranjan_ISWC11_2011.pdf</subfield>
			<subfield code="x">PUBLIC</subfield>
		</datafield>
		<datafield tag="711" ind1="2" ind2=" ">
			<subfield code="a">International Symposium on Wearable Computing</subfield>
		</datafield>
		<datafield tag="260" ind1=" " ind2=" ">
			<subfield code="c">2011</subfield>
		</datafield>
		<datafield tag="773" ind1=" " ind2=" ">
			<subfield code="c">8</subfield>
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		<datafield tag="520" ind1=" " ind2=" ">
			<subfield code="a">In this paper, we investigate the relationship between behavioral characteristics derived from rich smartphone data and self-reported personality traits. Our data stems from smartphones of a set of 83 individuals collected over a continuous period of 8 months. From the analysis, we show that aggregated features obtained from smartphone usage data can be indicators of the Big-Five personality traits. Additionally, we develop an automatic method to infer the personality type of a user based on cellphone usage using supervised learning. We show that our method performs significantly above chance and up to 75.9% accuracy. To our knowledge, this constitutes the first study on the analysis and classification of personality traits using smartphone data.</subfield>
		</datafield>
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