TV-slant presentatie_politicologen_etmaal
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TV-slant presentatie_politicologen_etmaal TV-slant presentatie_politicologen_etmaal Presentation Transcript

  • Bart de Goede Maarten Marx Political slant in public broadcasting 9 June 2011 Politicologenetmaal, Amsterdamwoensdag 8 juni 2011 (week )
  • Research aim • Frivolous research for a bachelor thesis • Research aim: Apply methodology Gentzkow & Shapiro (2010) to Dutch situation, perhaps improve using NLP • Future applications: • Analysis of Dutch media landscape (NewsMonitor) • Agendasetting and framing research (Timmermans, Breeman) • Parliament and media: lag or lead? (Vliegenthart)woensdag 8 juni 2011 (week )
  • Disclaimer • We are information scientists, not political scientists • We might have made awful conceptual mistakes • We will have missed almost all important referenceswoensdag 8 juni 2011 (week )
  • Disclaimer • Our aim is to show a powerful technique • We concentrated on getting the data ‘in shape’, rather than interpretation of resultswoensdag 8 juni 2011 (week )
  • Talk outline 1. Research plan and methodology 2. Description of our research 3. Results 4. What’s next?woensdag 8 juni 2011 (week )
  • Gentzkow & Shapiro • Econometrical research: compare language of news outlets to political language • ‘An economically significant demand for news slanted towards one’s own political ideology’ Gentzkow, M. and Shapiro, J. M. (2010). What drives media slant? Evi- dence from U.S. daily newspapers. Econometrica, 78(1):35–71.woensdag 8 juni 2011 (week )
  • Gentzkow & Shapiro Operationalization • Find characteristic words and phrases of Democrats and Republicans in Hansards (‘death tax’ versus ‘estate tax’) • Count relative frequencies of these words in newspapers • Score newspapers on ‘political slant’ by comparing frequencies of Democratic and Republican words • ... (even more, but not relevant to us)woensdag 8 juni 2011 (week )
  • Our research Reproduce, with some alterations • Dutch versus English: compound words, unigrams instead of bigrams • Television data instead of newspapers • Far more political parties • Other, more powerful technique for finding characteristic wordswoensdag 8 juni 2011 (week )
  • Our research An outline 1. Collecting TV data 2. Selecting appropriate broadcasts 3. Defining political groups 4. Obtaining data for each group 5. Obtaining characteristic words 6. Compare word use in political groups and TV broadcastswoensdag 8 juni 2011 (week )
  • TV Data • Subtitles for the hearing impaired (http://tt888.nl) • Complete data from January 2008 till February 2011 • Problem: hardly any useful metadata (63% only has date and time of broadcast)woensdag 8 juni 2011 (week )
  • TV Data Solution Before After Programme • TV guide with title 16.995 32.491 • Used http://tv2day.nl to Unique 4.560 -> combine broadcast time 2.238 titles 2.702 with (unambiguous) program title Single 1.598 1.174 events Broadcast frequency 1.104 1.064 >2woensdag 8 juni 2011 (week )
  • Selected broadcasts Nova 362.844 words Pauw & Witteman 895.935 words DWDD 1.626.929 words EenVandaag 1.556.642 words Nos Journaal 12.609.620 words Goedemorgen Nederland 760.658 words Netwerk 879.635 words NOS Jeugdjournaal 1.383.728 words Buitenhof DWDD EenVandaag Goedemorgen Nederland Het Elfde Uur Holland Doc Knevel en Van den Brink Netwerk Nieuwsuur NOS Jeugdjournaal Nos Journaal Nova Ochtendspits Pauw & Witteman PowNews SchoolTV Weekjournaal Sinterklaasjournaal Tegenlicht Uitgesproken Vragenuurtje Zemblawoensdag 8 juni 2011 (week )
  • Political groups • Parliamentary period with greatest overlap on TV data set: Balkenende IV • Experiments with e.g. Wordfish have shown that text comparisons mostly measure government - opposition, not left - right (Hirst et al., 2010) Hirst, G., Riabinin, Y., Graham, J., and Boizot-Roche, M. Text to Ideology or Text to Party Status?woensdag 8 juni 2011 (week )
  • Political groups • Therefore, we choose: • Government (CDA, PvdA and ChristenUnie) • Left wing opposition (GroenLinks, SP) • Right wing opposition (PVV, VVD)woensdag 8 juni 2011 (week )
  • Obtaining Proceedings data Trivial, using the PoliticalMashup database $collection//HAN1995// root[date restriction]// speech[@party matches(party names)]/p/text() Explain query: HAN1995: allwoensdag 8 juni 2011 (week ) since 1995
  • Characteristic words Parsimonious language model • Transform word frequency counts into probability distributions of words (maximum likelyhood estimation) • Compare distributions of subsets to distribution of all words • Choose words from subset whose frequency is much higher than expected λ(t|D) • Adjust probabilities et = tf (t, D) · (1 − λ)P (t|C) + λP (t|D) • Iterate to convergence P (t|D) = ￿ et t etwoensdag 8 juni 2011 (week )
  • Characteristic words Why take the trouble? • Filter out (corpus specific) ‘stopwords’ (e.g. ‘voorzitter’) • Remove noise (‘kopvoddentaks’ out, ‘sharia’ in)woensdag 8 juni 2011 (week )
  • In action Top 5 characteristic words left (SP, GroenLinks) right (PVV, VVD) leraar politie student crimineel kinderombudsman straf docent illegaal bonus boetewoensdag 8 juni 2011 (week )
  • In action Source: http://politiekinzicht.comwoensdag 8 juni 2011 (week )
  • In action Source: http://politiekinzicht.comwoensdag 8 juni 2011 (week )
  • In actionwoensdag 8 juni 2011 (week )
  • In actionwoensdag 8 juni 2011 (week )
  • In actionwoensdag 8 juni 2011 (week )
  • In actionwoensdag 8 juni 2011 (week )
  • Comparison 1. Find most characteristic words for each political group 2. For each political group, estimate the probability that an arbitrary word in a tv-programme is one of their characteristic words ￿ tft,T V ˆ P (q|T V ) = t∈q |T V |woensdag 8 juni 2011 (week )
  • Results DWDD 0,700 Estimated probability of words appearing 0,525 0,350 0,175 0 50 100 150 200 250 500 750 1000 1500 2000 2500 3000 n parsimonious derived words gov left right *condensed values on x-axiswoensdag 8 juni 2011 (week )
  • Results PowNews 0,700 Estimated probability of words appearing 0,525 0,350 0,175 0 50 100 150 200 250 500 750 1000 1500 2000 2500 3000 n parsimonious derived words gov left right *condensed values on x-axiswoensdag 8 juni 2011 (week )
  • Results News (Journaal, Ochtendspits, etc.) 0,040 Estimated probability of words appearing 0,030 0,020 0,010 0 50 100 150 200 250 n parsimonious derived words cda christenunie d66 groenlinks pvda pvdd pvv sgp sp verdonk vvdwoensdag 8 juni 2011 (week )
  • Results Talkshows 0,030 Cumulative probability of words appearing 0,023 0,015 0,008 0 50 100 150 200 250 n parsimonious derived words cda christenunie d66 groenlinks pvda pvdd pvv sgp sp verdonk vvdwoensdag 8 juni 2011 (week )
  • ‘Conclusions’ • Right never ‘wins’ • Possible explanations: • TV = left church • TV does not pick up right-wing slanted words • Or: is TV-language use not different from regular Dutch?woensdag 8 juni 2011 (week )
  • What’s next? • First, turn all this into a bachelor thesis (deadline in two weeks) • Future: • Team up with researcher(s) in political science and media analysis Candidates? • Try out more sophisticated NLP techniques • ... • Publish articlewoensdag 8 juni 2011 (week )
  • Questions? Slides available at http://www.politicalmashup.nlwoensdag 8 juni 2011 (week )