Russia Finland Reflection on Neighbours Next Door 1 June 2018 - PowerPoint PPT Presentation

May 13, 2023 •454 likes •723 views

Russia Finland Reflection on Neighbours Next Door 1 June 2018 Alexey Igonen, Arturs Polis Ilona Repponen, Miika Lampi Mila Oiva, Victoria Tkachenko Group leaders : Andrey Indukaev, Daria Gritsenko Research question What are the images of

Russia ⇔ Finland Reflection on Neighbours Next Door 1 June 2018 Alexey Igonen, Arturs Polis Ilona Repponen, Miika Lampi Mila Oiva, Victoria Tkachenko Group leaders : Andrey Indukaev, Daria Gritsenko
Research question What are the images of Finland in the Russian media and Russia in the Finnish media?
120.000+ articles Period: 1997-2017 Newspapers: Russian Federal Russian Regional YLE Finland
Case studies 1. News agenda 2. Dynamic geography 3. Understanding of the ‘neighbour’
What’s on the agenda? SPORTS and POLITICS
Finnish media (YLE)
Russian media (federal) Sports national team team match championship Politics EU child war Ukraine NATO
Where things happen?
Where things happen?
Where things happen?
Neighbour/Naapuri/Сосед What is neighbourhood? www.iltalehti.fi
Neighbour/Naapuri/Сосед That’s where ‘neighbour’ comes all political
Neighbour/Naapuri/Сосед That’s where ‘neighbour’ comes all political
Neighbour/Naapuri/Сосед That’s where neighbourhood comes all political
Neighbour/Naapuri/Сосед Patterns in color
Computational techniques
Data Cleaning json to csv lemmatization -> returning the words to their basic form removing the stop words -> the not meaningful “and”, “or”... Cincinnati Bell Historical Archives
Sports and Politics - techniques Dominant annual agendas TF-IDF The most significant word from each article
Where things happen? - techniques
Understanding the ‘neighbour’ - techniques W2V library nearby words - “use-synonyms” 5-year window clustering of nearby words
Challenges and Limitations Wordclouds , Method: TF-IDF - Lemmatization - Timestamps - Running TF-IDF on individual articles VS combined yearly data - 1 pass VS 2 pass TF-IDF - Can miss short-lived keywords!
Challenges and Limitations Geo Mapping and Topic Modelling , Method: POI mapping, STM - Lemmatization - Place name transliteration and disambiguation - Selecting the number of topics and topic clusters - Ambiguous articles and topics
Challenges and Limitations Word neighbourhoods , Method: Word2Vec - Timestamps and Lemmatization - Quantity of data for shorter periods (5 years at a time) - Picking the right dimensionality and threshold - Reading too much into it!
Ideas for future research Yearly words per topic - sports, culture, economics Compare seemingly similar concepts across languages - same or not? Causality and sentiment analysis - connect events to sentiment
Public outreach during the hackathon
Спасибо! Kiitos! Thank you! Questions?

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