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		<datafield tag="980" ind1=" " ind2=" ">
			<subfield code="a">CONF</subfield>
		</datafield>
		<datafield tag="970" ind1=" " ind2=" ">
			<subfield code="a">Biel_ICWSM_2012/IDIAP</subfield>
		</datafield>
		<datafield tag="245" ind1=" " ind2=" ">
			<subfield code="a">The Good, the Bad, and the Angry: Analyzing Crowdsourced Impressions of Vloggers</subfield>
		</datafield>
		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Biel, Joan-Isaac</subfield>
		</datafield>
		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Gatica-Perez, Daniel</subfield>
		</datafield>
		<datafield tag="653" ind1="1" ind2=" ">
			<subfield code="a">attractiveness</subfield>
		</datafield>
		<datafield tag="653" ind1="1" ind2=" ">
			<subfield code="a">Crowdsourcing</subfield>
		</datafield>
		<datafield tag="653" ind1="1" ind2=" ">
			<subfield code="a">mood</subfield>
		</datafield>
		<datafield tag="653" ind1="1" ind2=" ">
			<subfield code="a">Personality</subfield>
		</datafield>
		<datafield tag="653" ind1="1" ind2=" ">
			<subfield code="a">social media</subfield>
		</datafield>
		<datafield tag="653" ind1="1" ind2=" ">
			<subfield code="a">vlogs</subfield>
		</datafield>
		<datafield tag="856" ind1="4" ind2="0">
			<subfield code="i">EXTERNAL</subfield>
			<subfield code="u">http://publications.idiap.ch/attachments/papers/2012/Biel_ICWSM_2012.pdf</subfield>
			<subfield code="x">PUBLIC</subfield>
		</datafield>
		<datafield tag="711" ind1="2" ind2=" ">
			<subfield code="a">Proceedings of AAAI International Conference on Weblogs and Social Media</subfield>
		</datafield>
		<datafield tag="260" ind1=" " ind2=" ">
			<subfield code="c">2012</subfield>
		</datafield>
		<datafield tag="520" ind1=" " ind2=" ">
			<subfield code="a">We address the study of interpersonal perception in social conversational video based on multifaceted impressions collected from short video-watching. First, we crowdsourced the annotation of personality, attractiveness, and mood impressions for a dataset of YouTube vloggers, generating a corpora that has potential to develop automatic techniques for vlogger characterization. Then, we provide an analysis of the crowdsourced annotations focusing on the level of agreement among annotators, as well as the interplay between different impressions. Overall, this work provides interesting new insights on vlogger impressions and the use of crowdsourcing to collect behavioral annotations from multimodal data.</subfield>
		</datafield>
	</record>
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