{"id":"https://openalex.org/W4400695237","doi":"https://doi.org/10.1007/978-3-031-63536-6_10","title":"Finding Argument Fragments on Social Media with Corpus Queries and LLMs","display_name":"Finding Argument Fragments on Social Media with Corpus Queries and LLMs","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4400695237","doi":"https://doi.org/10.1007/978-3-031-63536-6_10"},"language":"en","primary_location":{"id":"doi:10.1007/978-3-031-63536-6_10","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-031-63536-6_10","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1007/978-3-031-63536-6_10","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5090070460","display_name":"Natalie Dykes","orcid":"https://orcid.org/0000-0002-4650-1455"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Nathan Dykes","raw_affiliation_strings":["Chair of Computational Corpus Linguistics, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Bismarckstr. 6, 91054, Erlangen, Germany"],"raw_orcid":"https://orcid.org/0000-0002-4650-1455","affiliations":[{"raw_affiliation_string":"Chair of Computational Corpus Linguistics, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Bismarckstr. 6, 91054, Erlangen, Germany","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016661742","display_name":"Stefan Evert","orcid":"https://orcid.org/0000-0002-4192-2437"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Stephanie Evert","raw_affiliation_strings":["Chair of Computational Corpus Linguistics, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Bismarckstr. 6, 91054, Erlangen, Germany"],"raw_orcid":"https://orcid.org/0000-0002-4192-2437","affiliations":[{"raw_affiliation_string":"Chair of Computational Corpus Linguistics, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Bismarckstr. 6, 91054, Erlangen, Germany","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078447039","display_name":"Philipp Heinrich","orcid":"https://orcid.org/0000-0002-4785-9205"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Philipp Heinrich","raw_affiliation_strings":["Chair of Computational Corpus Linguistics, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Bismarckstr. 6, 91054, Erlangen, Germany"],"raw_orcid":"https://orcid.org/0000-0002-4785-9205","affiliations":[{"raw_affiliation_string":"Chair of Computational Corpus Linguistics, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Bismarckstr. 6, 91054, Erlangen, Germany","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025243084","display_name":"Merlin Humml","orcid":"https://orcid.org/0000-0002-2251-8519"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Merlin Humml","raw_affiliation_strings":["Chair of Theoretical Computer Science, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Martensstr. 3, 91058, Erlangen, Germany"],"raw_orcid":"https://orcid.org/0000-0002-2251-8519","affiliations":[{"raw_affiliation_string":"Chair of Theoretical Computer Science, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Martensstr. 3, 91058, Erlangen, Germany","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073765060","display_name":"Lutz Schr\u00f6der","orcid":"https://orcid.org/0000-0002-3146-5906"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Lutz Schr\u00f6der","raw_affiliation_strings":["Chair of Theoretical Computer Science, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Martensstr. 3, 91058, Erlangen, Germany"],"raw_orcid":"https://orcid.org/0000-0002-3146-5906","affiliations":[{"raw_affiliation_string":"Chair of Theoretical Computer Science, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Martensstr. 3, 91058, Erlangen, Germany","institution_ids":["https://openalex.org/I181369854"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5090070460"],"corresponding_institution_ids":["https://openalex.org/I181369854"],"apc_list":{"value":5000,"currency":"EUR","value_usd":5392},"apc_paid":{"value":5000,"currency":"EUR","value_usd":5392},"fwci":0.3252,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.49006251,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"163","last_page":"181"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9997000098228455,"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/T10028","display_name":"Topic Modeling","score":0.9997000098228455,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9997000098228455,"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/T10456","display_name":"Multi-Agent Systems and Negotiation","score":0.9976000189781189,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8444105386734009},{"id":"https://openalex.org/keywords/argumentative","display_name":"Argumentative","score":0.738224446773529},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.6213400959968567},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.5994389057159424},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.5965476632118225},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5648609399795532},{"id":"https://openalex.org/keywords/recall","display_name":"Recall","score":0.5386682748794556},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5085452795028687},{"id":"https://openalex.org/keywords/social-media","display_name":"Social media","score":0.5062476992607117},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.4888853132724762},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4724690616130829},{"id":"https://openalex.org/keywords/argumentation-theory","display_name":"Argumentation theory","score":0.4679993987083435},{"id":"https://openalex.org/keywords/argument","display_name":"Argument (complex analysis)","score":0.4631504416465759},{"id":"https://openalex.org/keywords/referendum","display_name":"Referendum","score":0.4139474332332611},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.17711186408996582},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.170276939868927}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8444105386734009},{"id":"https://openalex.org/C2781306805","wikidata":"https://www.wikidata.org/wiki/Q4789761","display_name":"Argumentative","level":2,"score":0.738224446773529},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.6213400959968567},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.5994389057159424},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.5965476632118225},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5648609399795532},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.5386682748794556},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5085452795028687},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.5062476992607117},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4888853132724762},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4724690616130829},{"id":"https://openalex.org/C65059942","wikidata":"https://www.wikidata.org/wiki/Q270105","display_name":"Argumentation theory","level":2,"score":0.4679993987083435},{"id":"https://openalex.org/C98184364","wikidata":"https://www.wikidata.org/wiki/Q1780131","display_name":"Argument (complex analysis)","level":2,"score":0.4631504416465759},{"id":"https://openalex.org/C2781462389","wikidata":"https://www.wikidata.org/wiki/Q43109","display_name":"Referendum","level":3,"score":0.4139474332332611},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.17711186408996582},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.170276939868927},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/978-3-031-63536-6_10","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-031-63536-6_10","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"}],"best_oa_location":{"id":"doi:10.1007/978-3-031-63536-6_10","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-031-63536-6_10","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W329130461","https://openalex.org/W581684831","https://openalex.org/W2095503871","https://openalex.org/W2123661878","https://openalex.org/W2137023796","https://openalex.org/W2168579166","https://openalex.org/W2426119782","https://openalex.org/W2509343702","https://openalex.org/W2741125043","https://openalex.org/W2760347205","https://openalex.org/W2842153692","https://openalex.org/W2875864444","https://openalex.org/W2886987881","https://openalex.org/W2953402219","https://openalex.org/W2970641574","https://openalex.org/W3040582429","https://openalex.org/W3155407547","https://openalex.org/W3157362625","https://openalex.org/W3174828871","https://openalex.org/W3177058128","https://openalex.org/W3211973768","https://openalex.org/W3216056800","https://openalex.org/W4280534475","https://openalex.org/W4296959557","https://openalex.org/W4382239437","https://openalex.org/W4389195072"],"related_works":["https://openalex.org/W4321448273","https://openalex.org/W123909285","https://openalex.org/W2235416023","https://openalex.org/W4255580133","https://openalex.org/W2950795290","https://openalex.org/W2513082738","https://openalex.org/W3093095102","https://openalex.org/W1044030705","https://openalex.org/W1582835128","https://openalex.org/W2502751716"],"abstract_inverted_index":{"Abstract":[0],"We":[1,50,93,108],"are":[2],"concerned":[3],"with":[4,12,90,143],"extracting":[5],"argumentative":[6],"fragments":[7],"from":[8],"social":[9],"media,":[10],"exemplified":[11],"a":[13,17,68],"case":[14],"study":[15],"on":[16],"large":[18],"corpus":[19,37,40],"of":[20,54,71,86,88],"English":[21],"tweets":[22],"about":[23],"the":[24,36,133],"UK":[25],"Brexit":[26],"referendum":[27],"in":[28,46],"2016.":[29],"Our":[30],"overall":[31],"approach":[32],"is":[33,74],"to":[34,76,100,106,112],"parse":[35],"using":[38],"dedicated":[39],"queries":[41,82,96],"that":[42],"fill":[43],"designated":[44],"slots":[45],"predefined":[47],"logical":[48,55],"patterns.":[49],"present":[51],"an":[52,129],"inventory":[53],"patterns":[56],"and":[57,65,132,137],"corresponding":[58],"queries,":[59],"which":[60],"have":[61],"been":[62],"carefully":[63],"designed":[64],"refined.":[66],"While":[67],"gold":[69],"standard":[70],"substantial":[72],"size":[73],"difficult":[75],"obtain":[77],"by":[78],"manual":[79],"annotation,":[80],"our":[81],"can":[83,97,122,139],"retrieve":[84],"hundreds":[85],"thousands":[87],"examples":[89],"high":[91],"precision.":[92],"show":[94,110],"how":[95,111],"be":[98,123,140],"combined":[99],"extract":[101],"complex":[102],"nested":[103],"statements":[104],"relevant":[105],"argumentation.":[107],"also":[109],"proceed":[113],"for":[114,128],"applications":[115],"needing":[116],"higher":[117],"recall:":[118],"high-precision":[119],"query":[120],"matches":[121],"used":[124],"as":[125],"training":[126],"data":[127],"LLM":[130],"classifier,":[131],"trade-off":[134],"between":[135],"precision":[136],"recall":[138],"freely":[141],"adjusted":[142],"its":[144],"cutoff":[145],"threshold.":[146]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}
