Social Data in Academic Research


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This presentation shows how to use social media data and analysis in academic research in the field of marketing, economics, consumer behavior, and psychology. LiveWall ( collects social data and allows you to use it for research.

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  • Can we apply swarm theory to social data analysis? As individuals, we might not be able to hold onto or understand a dataset, but as a group, we can come at a dataset from different perspectives, look at very small parts, and then as an end result -- extract real, worthwhile meaning.
  • Social Data in Academic Research

    1. 1. Using social media data in academic research
    2. 2. LiveWall • Founded in 2011 by former Tilburg University students with years of experience in IT and media • Part of IntoApps and ServingSocial • Goal: to connect online and offline audiences & and to make social media visible & concrete
    3. 3. CustomersWe have a wide range of hundreds of users and customers,including brands, service providers, sports, event companies, B2B
    4. 4. LiveWall platform • Content aggregation platform for social media sources Data from Twitter, Facebook, Instagram, Foursquare, Youtube, Flickr • Display messages on screens For events, customer service, internal usage • Statistics of social media usage Metrics, text analysis etc.
    5. 5. Social media & data
    6. 6. 1 BILLIONNumber of Facebookusers in October 2012If Facebook were a country,it would be the third mostPopulated in the world
    7. 7. 1 BILLIONNumber of posts onFacebook per day2.7 BILLIONNumber of commentson Facebook per day
    8. 8. 500 MILLIONActive Twitter users340 MILLIONTweets per day
    9. 9. 72 HOURSOf video uploadedper minute1 TRILLIONVideo views per year
    10. 10. 4 BILLIONInstagram pictures500 MILLIONFacebook photos per day
    11. 11. At social media we talk about..
    12. 12. At social media we talk we work
    13. 13. At social media we talk we workwhat we know
    14. 14. At social media we talk we workwhat we knowhow we learn
    15. 15. At social media we talk we workwhat we knowhow we learnwho we like
    16. 16. At social media we talk we workwhat we knowhow we learnwho we likewhat interests us
    17. 17. At social media we talk we workwhat we know what we hatehow we learnwho we likewhat interests us
    18. 18. At social media we talk we workwhat we know what we hatehow we learn how we evaluatewho we likewhat interests us
    19. 19. At social media we talk we workwhat we know what we hatehow we learn how we evaluatewho we like our emotionswhat interests us
    20. 20. At social media we talk we workwhat we know what we hatehow we learn how we evaluatewho we like our emotionswhat interests us our life
    21. 21. ..and social media captures this social interaction digitally in huge databases
    22. 22. ..and social media captures this social interaction digitally in huge databases Why not use this in research in the field ofcommunication, marketing, economics and psychology?
    23. 23. Value starting to get noticed The Wall street Journal The Guardian The Boston Globe
    24. 24. What data is available?
    25. 25. Getting data• Social media platforms give access to their data – Through the use of API’s (Application Interfaces) – For external software – Easily download data and use it – In standardized formats per platform• LiveWall’s platform can load that data and process all data in a standard or custom format
    26. 26. Twitter: terminology• Follow: subscribe to see the posts of another user• Follower: someone who follows you• Tweet: one post of 140 characters• Hashtag: Word with # in front to categorize the post – People can follow one subject – Free to choose• Retweet: resend the message of someone else – Spread news• Trending topic: the most popular subjects• Mention: a tweet that addresses (mentions) another user in the post (with @ sign)
    27. 27. Twitter: available data• Tweets list – Tweet text and details based upon a query by hashtag, language, username or location – Number of tweets per hashtag• Individual tweet – Retweets – Assigned media: video/picture – Source: web/mobile• User – Number of followers, friends and favourites – Location data (limited) – Mentions – Description
    28. 28. Facebook: terminology• Post / status: one content element that can include link, image, video or just text• Like: someone gives positive feedback on a post, or says it wants to connect to the page or content• Timeline: the personal collection of photos, messages and experience of every user• (Fan)Page: page of a brand, company or event that includes posts, video’s, pictures and apps of a brand. On the fanpage both the holder of the page and the audience can communicate• Fan: Someone who likes your fanpage and thereby subscribes to receive all the updates that are placed at the Facebook page on their personal timeline
    29. 29. Facebook: available data• Fanpage – All posts – Likes and comments – Total number of fans – Photo’s, video’s• Fanpage (after approval of page administrator) – Impressions (view of any content), by source – Engagement: total of click, like respond etc. – Consumptions: people who clicked contnet – Fans by city, country, gender, age – Page views
    30. 30. Facebook: available data• Post (after approval of page administrator) – Impressions – Engagement – Consumptions – Negative feedback• User (after approval of user) – Birthday – Hometown – Work – Education – Languages – All friends – All liked pages and subjects – Location based checkins – All posts including text – Photo’s, video’s
    31. 31. What can LiveWall do• Collect metrics over time, E.g. – Hourly number of tweets per channel (Twitter, Instagram etc.) – Number of retweets per post – Impressions and engagement of Facebook page per day – Amount of social media posts by the company itself per day Etc.• Text analysis – Associations – Sentiment – Audience interests• Set up a custom application or construction for a research
    32. 32. Statistics: Sentiment / EmotionMetrics: frequency and channels
    33. 33. Text Analysis: AssociationsKnown associations are interesting for marketing,branding, campaigns & partnerships (#vodka)
    34. 34. Text Analysis: Associations
    35. 35. Statistics: Sentiment / EmotionSentiment
    36. 36. Examples• Is there more feedback on negative messages? (real data, experimental) – Sentiment and retweets and/or reach in variance analysis• What type of message has the best effect – Text / image vs shares/likes/retweets – Second person / first person vs shares/likes/retweets• Can social media feedback predict share value – Regression with share value over time, number of posts over time, post sentiment
    37. 37. Examples• Which brands clusters are there in industry X – Cluster analysis of associations of brands (Red Bull – Vodka)• What types of brands tend to have the biggest social media success – Compare brand groups based on followers, likes and posts about the brand• Do you have the same social relationships online as you have offline? – Compare interactions with friends online with the top rated friends offline
    38. 38. Examples• What is the difference between social content on Facebook in comparison to Twitter – Compare Twitter and Facebook accounts of one organisation and see how the reactions differ• Does peer endorsement work better than celebrity endorsement? – One part of the group sees Facebook friends that endorse a brand, the other part of the group sees celebrities to endorse the brand
    39. 39. Examples• What are the associations people make with certain product groups (for example sigarets )? - Input can be used for designing an effective campaign for or against usage of the product.• When a brand is associated in social media with a certain word like ‘amazing’ or ‘splendid’, does mentioning the word in campaigns or commercials lead to a higher liking?
    40. 40. Swarm Theory (Peter Miller)
    41. 41. Web WhereOnline? Eelco @bizzsms