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Social media analysis and document based research

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Jari Jussila
Jari Jussila

This document provides an overview of document-based research methods and examples of analyzing secondary data sources like social media for business intelligence purposes. It discusses using documents that were created for other purposes, issues to consider, and common public and company-specific data sources. Examples include analyzing the sentiment of tweets about companies; comparing Twitter sentiment to polls; examining language used by an organization and its audience on social media; and identifying emotions and communication styles in social media content.

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www.hamk.fi Social media analysis and document based research D.Sc. Jari Jussila @jjussila Introduction to Business Information Management 22.2.2019
About the presentation • This presentation has been originally compiled by Jari Jussila, Häme University of Applied Sciences, @jjussila • Jukka Huhtamäki, @jnkka, updated the presentation and gave this lecture at Tampere University TTA-15090 Research Methodology course during 31 January 2019 • Jari Jussila translated the presentation, and made few updates for lecture on BBIBP18 Introduction to Business Information Management at Häme University of Applied Sciences • Lecture material from Professor Saku Mäkinen (2016) was also used in compiling this lecture
www.hamk.fi Document based research • Created (and collected) for different purpose, i.e. secondary data • Due to increased computational capacity it is more easy to collect and store documents • Few things to consider related to document based research � if documents have been already collected, you save time and effort � when the documents have been collected for a different purpose, you may not find an answer to your question Adapted from Mäkinen (2016)
www.hamk.fi Documents as a source of business information - social media - web pages - press releases and bulletins - annual and quarterly reports - databases - intranet - documents - discussions (audio, video, chat) - statistics - reports - studies - commercial statistics - commercial reports - studies requiring membership or affiliation to access public not public Companyspecificgeneral Adapted from Mäkinen (2016)
www.hamk.fi Common public sources • Statistics, e.g. • Statistics Finland: http://www.stat.fi/ • Open Data: https://www.avoindata.fi/fi • Links to Open Data Sources and Pages: https://avoinhäme.fi/avoin-data-aineisto/ • European Statistics: http://epp.eurostat.ec.europa.eu/ • Finnish Social Science Data Archive: https://www.fsd.uta.fi/ • Public data sources are used in writing a thesis: • In the introduction section to argument the significance of the topic • As part of a literature review and theory building • Or to support empirical material • Public data sources can be also the main empirical material, when e.g. • Determining market potential • Reviewing competition Adapted from Mäkinen (2016)
www.hamk.fi Quarterly Reports • Text visualization of quarterly reports as a source of competitive intelligence (CI) • Case study of three mobile phone manufacturers from the years 2000-2001 • Nokia • Motorola • Ericsson Source: Magnusson 2010
www.hamk.fi Impact of Facebook on stock markets • How Facebook discussions and activities impact different investor groups investing behavior • Evidence was found that less professional investors (households and non-profit organizations) investing behavior (purchase of stocks) is influenced by Facebook discussions and activities (e.g. Likes) Source: Siikanen et al. 2017
www.hamk.fi Event Study • According to efficient market hypothesis share prices fully reflect all available information, thus it can be observed how public information influence share prices • By calculating on event day (+/- 1 days) how the share price change was different from comparison groups market change we get the estimate of particular company’s share price change that is due to announcement/news/etc. event Nokia Corporation share price 14-20 Feb (Source: Nordnet) Using Secondary Data in Operations Management Research: Overview and Research Opportunities (Source: Singhal 2016, p. 25)
www.hamk.fi Data collected with crawlers and scrapers • Data can be collected from any web page • For example, crawler and scraper implemented to collect data from Indiegogo crowdfunding platform • Source code available from: http://github.com/jukkahuhtamaki/cr owdfunding-data • The code must be rewritten. Why could that be? Source: Huhtamäki et al. 2015
www.hamk.fi Data collected and analysed from social media content Source: https://underhood.co/hamk-university-of-applied-sciences
www.hamk.fi TUT (TUNI) as an example of challenges of analyzing social media content https://underhood.co/tampereen-teknillinen-yliopisto-(tty)
www.hamk.fi An analysis of language used by HAMK and its audience Source: https://underhood.co/hamk-university-of-applied-sciences
www.hamk.fi Sentiment analysis of social media content Jalonen 2016, Helo & Jalonen 2018
www.hamk.fi Analysis of sentiment and emotions of social media content 13. Tunnetilojen tunnistaminen Twitteristä. Jari Jussila, Mika Boedeker, Nina Helander & Vilma Vuori 14. Tunnistaako kone tunteesi? Sävyanalyysi sosiaalisen median sisältöjen tulkinnassa. Tuomo Helo & Harri Jalonen Available from: https://vastapaino.fi/sivu/tuote/twitter-viestintana/2442557
www.hamk.fi Sentiment analysis of tweets about IBM 21.2.2019 Source: Dejan Trifunovic 2019 Business Analytics and Business Intelligence
www.hamk.fi Twitter sentiment versus Gallup Poll of Consumer Confidence Source: Brendan O'Connor, Ramnath Balasubramanyan, Bryan R. Routledge, and Noah A. Smith. 2010. From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series. In ICWSM-2010
www.hamk.fi Framework of affective experiences and affective families Source: Jussila et al. 2018
www.hamk.fi Communication styles in Twitter • Energy sector and climate change related Twitter discussions were analyzed • Who, and what kind of communication styles were found? • Communication styles of tweets • Source: Ketonen-Oksi & Jalonen 2017
www.hamk.fi Communication styles & profiles Source: Ketonen-Oksi & Jalonen 2017 Image Couler @ pixabay

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Social media analysis and document based research

  • 1. www.hamk.fi Social media analysis and document based research D.Sc. Jari Jussila @jjussila Introduction to Business Information Management 22.2.2019
  • 2. About the presentation • This presentation has been originally compiled by Jari Jussila, Häme University of Applied Sciences, @jjussila • Jukka Huhtamäki, @jnkka, updated the presentation and gave this lecture at Tampere University TTA-15090 Research Methodology course during 31 January 2019 • Jari Jussila translated the presentation, and made few updates for lecture on BBIBP18 Introduction to Business Information Management at Häme University of Applied Sciences • Lecture material from Professor Saku Mäkinen (2016) was also used in compiling this lecture
  • 3. www.hamk.fi Document based research • Created (and collected) for different purpose, i.e. secondary data • Due to increased computational capacity it is more easy to collect and store documents • Few things to consider related to document based research � if documents have been already collected, you save time and effort � when the documents have been collected for a different purpose, you may not find an answer to your question Adapted from Mäkinen (2016)
  • 4. www.hamk.fi Documents as a source of business information - social media - web pages - press releases and bulletins - annual and quarterly reports - databases - intranet - documents - discussions (audio, video, chat) - statistics - reports - studies - commercial statistics - commercial reports - studies requiring membership or affiliation to access public not public Companyspecificgeneral Adapted from Mäkinen (2016)
  • 5. www.hamk.fi Common public sources • Statistics, e.g. • Statistics Finland: http://www.stat.fi/ • Open Data: https://www.avoindata.fi/fi • Links to Open Data Sources and Pages: https://avoinhäme.fi/avoin-data-aineisto/ • European Statistics: http://epp.eurostat.ec.europa.eu/ • Finnish Social Science Data Archive: https://www.fsd.uta.fi/ • Public data sources are used in writing a thesis: • In the introduction section to argument the significance of the topic • As part of a literature review and theory building • Or to support empirical material • Public data sources can be also the main empirical material, when e.g. • Determining market potential • Reviewing competition Adapted from Mäkinen (2016)
  • 6. www.hamk.fi Quarterly Reports • Text visualization of quarterly reports as a source of competitive intelligence (CI) • Case study of three mobile phone manufacturers from the years 2000-2001 • Nokia • Motorola • Ericsson Source: Magnusson 2010
  • 7. www.hamk.fi Impact of Facebook on stock markets • How Facebook discussions and activities impact different investor groups investing behavior • Evidence was found that less professional investors (households and non-profit organizations) investing behavior (purchase of stocks) is influenced by Facebook discussions and activities (e.g. Likes) Source: Siikanen et al. 2017
  • 8. www.hamk.fi Event Study • According to efficient market hypothesis share prices fully reflect all available information, thus it can be observed how public information influence share prices • By calculating on event day (+/- 1 days) how the share price change was different from comparison groups market change we get the estimate of particular company’s share price change that is due to announcement/news/etc. event Nokia Corporation share price 14-20 Feb (Source: Nordnet) Using Secondary Data in Operations Management Research: Overview and Research Opportunities (Source: Singhal 2016, p. 25)
  • 9. www.hamk.fi Data collected with crawlers and scrapers • Data can be collected from any web page • For example, crawler and scraper implemented to collect data from Indiegogo crowdfunding platform • Source code available from: http://github.com/jukkahuhtamaki/cr owdfunding-data • The code must be rewritten. Why could that be? Source: Huhtamäki et al. 2015
  • 10. www.hamk.fi Data collected and analysed from social media content Source: https://underhood.co/hamk-university-of-applied-sciences
  • 11. www.hamk.fi TUT (TUNI) as an example of challenges of analyzing social media content https://underhood.co/tampereen-teknillinen-yliopisto-(tty)
  • 12. www.hamk.fi An analysis of language used by HAMK and its audience Source: https://underhood.co/hamk-university-of-applied-sciences
  • 13. www.hamk.fi Sentiment analysis of social media content Jalonen 2016, Helo & Jalonen 2018
  • 14. www.hamk.fi Analysis of sentiment and emotions of social media content 13. Tunnetilojen tunnistaminen Twitteristä. Jari Jussila, Mika Boedeker, Nina Helander & Vilma Vuori 14. Tunnistaako kone tunteesi? Sävyanalyysi sosiaalisen median sisältöjen tulkinnassa. Tuomo Helo & Harri Jalonen Available from: https://vastapaino.fi/sivu/tuote/twitter-viestintana/2442557
  • 15. www.hamk.fi Sentiment analysis of tweets about IBM 21.2.2019 Source: Dejan Trifunovic 2019 Business Analytics and Business Intelligence
  • 16. www.hamk.fi Twitter sentiment versus Gallup Poll of Consumer Confidence Source: Brendan O'Connor, Ramnath Balasubramanyan, Bryan R. Routledge, and Noah A. Smith. 2010. From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series. In ICWSM-2010
  • 17. www.hamk.fi Framework of affective experiences and affective families Source: Jussila et al. 2018
  • 18. www.hamk.fi Communication styles in Twitter • Energy sector and climate change related Twitter discussions were analyzed • Who, and what kind of communication styles were found? • Communication styles of tweets • Source: Ketonen-Oksi & Jalonen 2017
  • 19. www.hamk.fi Communication styles & profiles Source: Ketonen-Oksi & Jalonen 2017 Image Couler @ pixabay