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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