Historical Analysis of New York Times Foreign News using Semi-Supervised Models. Kohei Watanabe WIAS

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1 Historical Analysis of New York Times Foreign News using Semi-Supervised Models Kohei Watanabe WIAS

2 How can we measure opinions in the past? The goal of this project is to quantify the level of threat perceived by the Americans Perceived security environment explains US foreign policy A join project based at the IR department of the LSE There are datasets about security threat based on events (conflicts) and macro statistics (military, economy, demography etc.) We use the New York Times Archive Available via free Web-API It covers events from 1851 to today Only summary (lead sentences) is available in the free API Only little meta-data is available

3 Data Overview of international news system

4 Brief history of telegraph network Telegraph was invented by the British (William Cooke and Charles Wheatstone) and the American (Finley Morse) in 1837 Telegram reached the US west coast in 1861 The first trans-atlantic cable was laid first successfully in 1866 French telegraph system was exported to Japan in 1869 The network reached Nagasaki and Xiamen in 1871 as an extension of London-Vladivostok Great Northern telegraph cable

5 Telegraph network in

6 International news organizations News agencies Agence France-Presse (1835 ) Associated Press (1848) Reuters (1851) Newspapers The Times of London (1785) New York Times (1851)

7 News is not the mirror of the real world Foreign news reporting is a long chain of communication from sources to news audiences The seminal paper by Galtung and Ruge (1965) in the Journal of Peace Research Scholars agree that media image of the world is constrained by a set of criterion that guide journalists news selection Commercial news media try to match their coverage and audience interests Readers of the New York Times are the political and economic elite who have vested interest in events that may affect their activities News media cover not only powerful countries but also less powerful counties when potentially threatening events occur (e.g. unrests, wars, crimes, coups and disasters)

8 Data collection Search the NYT Archive Query keywords are threat-related keywords {threat*, danger*, fear*, risk*} Retrieval is full-text search Use R package rtimes to access the Web-API Collected 387,896 news summaries The two great German Powers have consented to pause in their career of conquest. After invading and taking possession of the Duchies, and just on the borders of Jutland,... (9 March 1864)

9 Analysis Textual data analysis using semi-supervised models

10 1860s

11 1900s

12 1910s

13 1940s

14 2010s

15 Classification by semi-supervised models By country Use newsmap to discover unknow geographical features There are too many names of places, people and organizations during the period Identify main geographical focus (single membership) By hostility Scale documents on hostility-friendly dimension There are articles about friendly military relations that do not pose threats Use LSS for word-embedding based document scaling

16 Newsmap A semi-supervised model for geographical news classification Use seed dictionary (in English, Japanese, German, Spanish, Russian, Chinese) Added historical names (e.g. Ottoman Empire and Leningrad) Extract and weight unknown words associated with seed words to improve classification result Can be use for other tasks (e.g. topic classification) depending on the seed dictionary newsmap is available on CRAN

17 Extracted geographical features for Japan Features Weight Features Weight Japanese Diet 3.44 Japan Mukden 3.33 Tokyo Manchuria 3.30 TOKYO 8.68 Japanese_Army 3.06 Manchukuo 4.60 Hankow 3.02 North_China 4.03 Pearl_Harbor 2.99 Tokio 3.71 Chungking 2.96 Tientsin 3.70 Shantung 2.95 Okinawa 3.49 Manchurian 2.91 Japanese_Government 3.48 Shanghai 2.83

18 Latent Semantic Scaling (LSS) A semi-supervised model for document scaling Use Latent Semantic Analysis (LSA) to construct a less noisy vector space model from the texts GloVe or Word2vec could be used but LSA is used for simplicity Seed words are {enemy, foe} for hostility and {friend, ally} for friendliness LSS needs only a few seeds words thanks to word-embedding LSS package is available on my Github page

19 The concept of spatial weighting Word w 2 Hostility seeds Word w 1 Friendly seeds

20 The result of LSS scaling

21 China, Russia

22 Germany, Japan

23 France, United Kingdom

24 Canada, Cuba, Spain, Mexico

25 Afghanistan, Iraq, Iran, Syria and Vietnam

26 Conclusions NYT API is very useful in historical research in IR You should understand the nature of the dataset Working of international news media/journalist is the data generation process LSS and Newsmap can produce intuitive results Semi-supervised models are very efficient with large complex datasets Needs cross-validation against other dataset (e.g. GDELT, The Correlates of Wars, however.

27 Additional information R packages rtimes Package that provides easy access to the New York Times API from R quanteda High-performance toolkit for quantitative analysis of textual data (on CRAN) newsmap Dictionary-based semi-supervised model for document classification (on CRAN) LSS Word embedding-based semi-supervised model for document scaling ( Events POLTEXT 2019 International symposium on quantitative text analysis Sept 2019 at Waseda University (