{"id":"https://openalex.org/W2014112886","doi":"https://doi.org/10.1145/2663204.2663237","title":"Why We Watch the News","display_name":"Why We Watch the News","publication_year":2014,"publication_date":"2014-11-12","ids":{"openalex":"https://openalex.org/W2014112886","doi":"https://doi.org/10.1145/2663204.2663237","mag":"2014112886"},"language":"en","primary_location":{"id":"doi:10.1145/2663204.2663237","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2663204.2663237","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 16th International Conference on Multimodal Interaction","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102907489","display_name":"Joseph G. Ellis","orcid":"https://orcid.org/0009-0004-7803-8778"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joseph G. Ellis","raw_affiliation_strings":["Columbia University, New York City, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Columbia University, New York City, NY, USA","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006517585","display_name":"Brendan Jou","orcid":"https://orcid.org/0000-0001-8033-0330"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Brendan Jou","raw_affiliation_strings":["Columbia University, New York City, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Columbia University, New York City, NY, USA","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037340457","display_name":"Shih\u2010Fu Chang","orcid":"https://orcid.org/0000-0003-1444-1205"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shih-Fu Chang","raw_affiliation_strings":["Columbia University, New York City, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Columbia University, New York City, NY, USA","institution_ids":["https://openalex.org/I78577930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I78577930"],"apc_list":null,"apc_paid":null,"fwci":0.8271,"has_fulltext":false,"cited_by_count":24,"citation_normalized_percentile":{"value":0.72933754,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"104","last_page":"111"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11439","display_name":"Video Analysis and Summarization","score":0.9958999752998352,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11795","display_name":"Humor Studies and Applications","score":0.9901000261306763,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8435656428337097},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.7055132389068604},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.6807584166526794},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.46188491582870483},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.45581430196762085},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.41545921564102173},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39123401045799255}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8435656428337097},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.7055132389068604},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.6807584166526794},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.46188491582870483},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.45581430196762085},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.41545921564102173},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39123401045799255},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2663204.2663237","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2663204.2663237","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 16th International Conference on Multimodal Interaction","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.46000000834465027,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1583620810","https://openalex.org/W2028988230","https://openalex.org/W2036751297","https://openalex.org/W2055531799","https://openalex.org/W2067577150","https://openalex.org/W2079725295","https://openalex.org/W2085662862","https://openalex.org/W2097726431","https://openalex.org/W2098689807","https://openalex.org/W2100127573","https://openalex.org/W2101231430","https://openalex.org/W2122563357","https://openalex.org/W2130162821","https://openalex.org/W2132719147","https://openalex.org/W2138761194","https://openalex.org/W2145310492","https://openalex.org/W2153635508","https://openalex.org/W2163352848","https://openalex.org/W2163808566","https://openalex.org/W2251939518","https://openalex.org/W2325168940","https://openalex.org/W2611532106","https://openalex.org/W2620858438","https://openalex.org/W3097096317","https://openalex.org/W4205184193","https://openalex.org/W6657566997"],"related_works":["https://openalex.org/W2548633793","https://openalex.org/W2941935829","https://openalex.org/W3089396779","https://openalex.org/W2596247554","https://openalex.org/W4301373556","https://openalex.org/W3013279174","https://openalex.org/W3132372214","https://openalex.org/W4224284088","https://openalex.org/W4286571989","https://openalex.org/W2369835347"],"abstract_inverted_index":{"We":[0,43,124,150],"present":[1,151],"a":[2,8,158],"multimodal":[3,106],"sentiment":[4,35,109,133,141,160],"study":[5],"performed":[6],"on":[7,90,121,175],"novel":[9],"collection":[10],"of":[11,24,39,52,83,102,131,162],"videos":[12],"mined":[13],"from":[14,55,138],"broadcast":[15,40],"and":[16,50,62,72,111,153,165],"cable":[17],"television":[18],"news":[19,56,98],"programs.":[20],"To":[21],"the":[22,29,37,48,73,100,132,144,180,186],"best":[23],"our":[25,45],"knowledge,":[26],"this":[27],"is":[28,148],"first":[30],"dataset":[31,184],"released":[32],"for":[33,47,80,97,108,135],"studying":[34],"in":[36,112],"domain":[38],"video":[41,74,99,145],"news.":[42],"describe":[44],"algorithm":[46],"processing":[49],"creation":[51],"person-specific":[53],"segments":[54],"video,":[57],"yielding":[58],"929":[59],"sentence-length":[60],"videos,":[61],"are":[63,77],"annotated":[64,79],"via":[65],"Amazon":[66],"Mechanical":[67],"Turk.":[68],"The":[69],"spoken":[70],"transcript":[71],"content":[75],"itself":[76,147],"each":[78],"their":[81,139],"expression":[82],"positive,":[84,163],"negative":[85,164],"or":[86],"neutral":[87],"sentiment.":[88],"Based":[89],"these":[91],"gathered":[92],"user":[93],"annotations,":[94],"we":[95,178],"demonstrate":[96],"importance":[101],"taking":[103],"into":[104],"account":[105],"information":[107],"prediction,":[110],"particular,":[113],"challenging":[114],"previous":[115],"text-based":[116],"approaches":[117],"that":[118,126],"rely":[119],"solely":[120],"available":[122],"transcripts.":[123],"show":[125],"as":[127,129,167,169],"much":[128],"21.54%":[130],"annotations":[134,142],"transcripts":[136],"differ":[137],"respective":[140],"when":[143],"clip":[146],"presented.":[149],"audio":[152],"visual":[154],"classification":[155,173],"baselines":[156],"over":[157],"three-way":[159],"prediction":[161],"neutral,":[166],"well":[168],"person-dependent":[170],"versus":[171],"person-independent":[172],"influence":[174],"performance.":[176],"Finally,":[177],"release":[179],"News":[181],"Rover":[182],"Sentiment":[183],"to":[185],"greater":[187],"research":[188],"community.":[189]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}