FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4311885717> ?p ?o ?g. }

• W4311885717 endingPage "313" @default.
• W4311885717 startingPage "289" @default.
• W4311885717 abstract "As part of the text-as-data movement, Natural Language Processing (NLP) provides a computational way to examine political polarization. We conducted a methodological scoping review of studies published since 2010 (n = 154) to clarify how NLP research has conceptualized and measured political polarization, and to characterize the degree of integration of the two different research paradigms that meet in this research area. We identified biases toward US context (59%), Twitter data (43%) and machine learning approach (33%). Research covers different layers of the political public sphere (politicians, experts, media, or the lay public), however, very few studies involved more than one layer. Results indicate that only a few studies made use of domain knowledge and a high proportion of the studies were not interdisciplinary. Those studies that made efforts to interpret the results demonstrated that the characteristics of political texts depend not only on the political position of their authors, but also on other often-overlooked factors. Ignoring these factors may lead to overly optimistic performance measures. Also, spurious results may be obtained when causal relations are inferred from textual data. Our paper provides arguments for the integration of explanatory and predictive modeling paradigms, and for a more interdisciplinary approach to polarization research.The online version contains supplementary material available at 10.1007/s42001-022-00196-2." @default.
• W4311885717 created "2023-01-02" @default.
• W4311885717 creator A5013127864 @default.
• W4311885717 date "2022-12-19" @default.
• W4311885717 modified "2023-10-13" @default.
• W4311885717 title "A scoping review on the use of natural language processing in research on political polarization: trends and research prospects" @default.
• W4311885717 cites W1119807432 @default.
• W4311885717 cites W1973193168 @default.
• W4311885717 cites W1980565545 @default.
• W4311885717 cites W2038889057 @default.
• W4311885717 cites W2041314074 @default.
• W4311885717 cites W2066387397 @default.
• W4311885717 cites W2084341220 @default.
• W4311885717 cites W2137029365 @default.
• W4311885717 cites W2149537132 @default.
• W4311885717 cites W2179362017 @default.
• W4311885717 cites W2250773804 @default.
• W4311885717 cites W2251159224 @default.
• W4311885717 cites W2252072508 @default.
• W4311885717 cites W2266547493 @default.
• W4311885717 cites W2308341436 @default.
• W4311885717 cites W2478081149 @default.
• W4311885717 cites W2532505703 @default.
• W4311885717 cites W2538660910 @default.
• W4311885717 cites W2593408211 @default.
• W4311885717 cites W2751738735 @default.
• W4311885717 cites W2767428878 @default.
• W4311885717 cites W2781364838 @default.
• W4311885717 cites W2792053130 @default.
• W4311885717 cites W2795504737 @default.
• W4311885717 cites W2810702571 @default.
• W4311885717 cites W2889771151 @default.
• W4311885717 cites W2894754391 @default.
• W4311885717 cites W2898958889 @default.
• W4311885717 cites W2899528563 @default.
• W4311885717 cites W2900938513 @default.
• W4311885717 cites W2903056428 @default.
• W4311885717 cites W2915591346 @default.
• W4311885717 cites W2921615403 @default.
• W4311885717 cites W2924611062 @default.
• W4311885717 cites W2945656617 @default.
• W4311885717 cites W2946054101 @default.
• W4311885717 cites W2953088889 @default.
• W4311885717 cites W2963834345 @default.
• W4311885717 cites W2972709922 @default.
• W4311885717 cites W2978632630 @default.
• W4311885717 cites W2982537810 @default.
• W4311885717 cites W2996219887 @default.
• W4311885717 cites W3006872574 @default.
• W4311885717 cites W3010139421 @default.
• W4311885717 cites W3015287569 @default.
• W4311885717 cites W3022516804 @default.
• W4311885717 cites W3033229230 @default.
• W4311885717 cites W3037619012 @default.
• W4311885717 cites W3039660108 @default.
• W4311885717 cites W3043494686 @default.
• W4311885717 cites W3049109863 @default.
• W4311885717 cites W3053276149 @default.
• W4311885717 cites W3092024150 @default.
• W4311885717 cites W3092240026 @default.
• W4311885717 cites W3093754687 @default.
• W4311885717 cites W3101859073 @default.
• W4311885717 cites W3138497854 @default.
• W4311885717 cites W3153071935 @default.
• W4311885717 cites W3154999740 @default.
• W4311885717 cites W3171138532 @default.
• W4311885717 cites W3174174150 @default.
• W4311885717 cites W3179346037 @default.
• W4311885717 cites W4205651423 @default.
• W4311885717 cites W4206453119 @default.
• W4311885717 cites W4221165557 @default.
• W4311885717 cites W4249265861 @default.
• W4311885717 cites W4256028900 @default.
• W4311885717 cites W425647333 @default.
• W4311885717 cites W4287112267 @default.
• W4311885717 cites W4289548559 @default.
• W4311885717 cites W4294973491 @default.
• W4311885717 cites W68298479 @default.
• W4311885717 cites W1971636079 @default.
• W4311885717 doi "https://doi.org/10.1007/s42001-022-00196-2" @default.
• W4311885717 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36568020" @default.
• W4311885717 hasPublicationYear "2022" @default.
• W4311885717 type Work @default.
• W4311885717 citedByCount "3" @default.
• W4311885717 countsByYear W43118857172023 @default.
• W4311885717 crossrefType "journal-article" @default.
• W4311885717 hasAuthorship W4311885717A5013127864 @default.
• W4311885717 hasBestOaLocation W43118857171 @default.
• W4311885717 hasConcept C119857082 @default.
• W4311885717 hasConcept C144024400 @default.
• W4311885717 hasConcept C147789679 @default.
• W4311885717 hasConcept C154945302 @default.
• W4311885717 hasConcept C17744445 @default.
• W4311885717 hasConcept C185592680 @default.
• W4311885717 hasConcept C199539241 @default.
• W4311885717 hasConcept C205049153 @default.
• W4311885717 hasConcept C2522767166 @default.
• W4311885717 hasConcept C2779610281 @default.

• next