Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Twitter and Facebook Data Mining Solutions

2,990 views

Published on

The growing popularity of ‘big data’ in the technobabble lexicon obscures the fact that the size of your archives should never stop you from including more quantitative analysis in your communications planning. Social networking sites, Facebook and Twitter in particular, offer incredibly accessible data at very low or no cost. While some analysis software does require extensive knowledge of at least one code language, we will look at how tools such as Google Spreadsheet and Microsoft Excel can be used to help expand insights from the native dashboards. Also, we will look at free analysis tools, such as Netlytic and Textalytics that allow users with no coding experience to generate stunning data visualizations of impressive complexity.

Published in: Data & Analytics
  • D0WNL0AD FULL ▶ ▶ ▶ ▶ http://1lite.top/3cyGX ◀ ◀ ◀ ◀
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
  • I’ve personally never heard of companies who can produce a paper for you until word got around among my college groupmates. My professor asked me to write a research paper based on a field I have no idea about. My research skills are also very poor. So, I thought I’d give it a try. I chose a writer who matched my writing style and fulfilled every requirement I proposed. I turned my paper in and I actually got a good grade. I highly recommend ⇒ www.HelpWriting.net ⇐
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
  • D0WNL0AD FULL ▶ ▶ ▶ ▶ http://1lite.top/3cyGX ◀ ◀ ◀ ◀
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here

Twitter and Facebook Data Mining Solutions

  1. 1. Fueling the Data Drive: Facebook & Twitter Data Mining Solutions for Digital Communicators Adrian J. Ebsary @AJEbsary
  2. 2. The Content Marketing Process
  3. 3. Starting your archives • Never trust your data to a third party • Own your own Excel (.CSV) files • Don’t settle for time-slice dashboards
  4. 4. Starting your archives • Keep regular export files on recurring basis (monthly) • If possible, segregate data by keyword/keyword families • Master archives can get bulky • May need to separate over time periods (yearly) • Might be errors in combining files or deletion of duplicates • If size is an issue, keep daily over weekly/monthly data
  5. 5. Excel with Excel • Plenty of free resources to learn • Google: “learn how to use Excel”
  6. 6. Excel with Excel • Plenty of free resources to learn • Google: “learn how to use Excel” • Understand IF statements • FIND, SEARCH, MID, LEFT, RIGHT, LEN • Find text by characters, position or string length • CONCATENATE • Paste text together • Excel time vs. Unix Time • Unix time easier to use for mathematical operations • Unix: Number of seconds since 1st of January, 1970
  7. 7. Data by network by ease-of-access • Facebook • No detailed data on user-generated content • Powerful generic interaction and reach analytics • Completely free, from the source (Facebook Insights) • Third-party offerings (usually) offer little more
  8. 8. Data by network by ease-of-access • Google+ • Integrate Google+ Pages into Google Analytics
  9. 9. Data by network by ease-of-access • Twitter (Tweets by Keyword) • Build your own app or…
  10. 10. Data by network by ease-of-access • Money, money, money • dev.twitter.com/programs/twitter-certified-products • More options, more networks, more $$$
  11. 11. Data by network by ease-of-access • Twitter (Account interaction data) • Want Twitter data? Buy a $10 ad.
  12. 12. Data by network by ease-of-access • Klout • Regular algorithm changes = little value beyond bragging rights • Multiple, regular scoring changes confuse scoring • Lack of transparency surrounding algorithm • One number to rule them all? • Multiple social networks simplified to single logarithmic scale • Benchmarking a moving target
  13. 13. Facebook Data
  14. 14. Facebook: NewsFeed Algorithm
  15. 15. Facebook: NewsFeed Algorithm • Negative Feedback kills post reach (anti-weight) • Hide post • Unfollow page/person • “I don’t want to see this” • Report as spam
  16. 16. Facebook: NewsFeed Algorithm • The vanishing ‘Virality’ score • Efficiency of attention consumption > overall reach • Virality: Total engagements / Total reach x 100% • Engagements = Comments + Likes + Clicks • Every pageload that results in no engagement = lost affinity • Boring content cuts your future reach potential
  17. 17. Facebook Insights Dashboard: Like Spikes • Watch for like spikes and identify source • Correlate with posts or events for additional insights
  18. 18. Facebook Insights Dashboard: Like Spikes • Watch for like spikes and identify source • Correlate with posts or events for additional insights
  19. 19. Facebook Insights Dashboard: Unlikeable days • Separating posts by at least one day clarifies unlike spikes • Anecdotally, unlikes often correlate with high like spikes • Low relative number of daily likes with high unlikes indicates highly ineffective content
  20. 20. Facebook Page-Specific Data: Virality • Virality = Engaged users / Total Reach * 100% • More accurate: Total ORGANIC Reach • Paid reach has less impact on affinity score
  21. 21. Facebook Page-Specific Data: Virality • Virality = Engaged users / Total Reach * 100% • More accurate: Total ORGANIC Reach • Paid reach has less impact on affinity score • Consumers vs. Engaged users? • http://www.jonloomer.com/2013/03/11/facebook-insights-consumer-vs-engaged-user/ • Consumer = interacted with your posts • Engaged user = interacted with your posts OR your page • Best: Daily Page Consumptions / Daily Organic Reach x 100%
  22. 22. Facebook Page-Specific Data: Unlikeable days • Negative feedback • Hidden from dashboard – need to download! • Identify affinity-killing days to find posts in need of improving • Look for high unlikes + negative feedback on single day
  23. 23. Facebook Page-Specific Data: Unlikeable days • Negative feedback • Account for reach • http://simplymeasured.com/blog/2013/05/30/negative-feedback-on-facebook-what- is-it-and-when-you-should-worry/
  24. 24. Facebook Post-Specific Data • Use to complement page-level analysis • Easier to visualize impact using daily metrics from page-level data • Graphing with posts as x-axis may obscure smaller data points, hide multi-day effects • Best: tag posts with the date page-leve data, date as x-axis
  25. 25. Twitter Data: Give ‘em your card • Use $10 to buy ad, get permanent access to analytics.Twitter.com • 30 day window on follows, unfollows, mentions
  26. 26. Twitter Data: Give ‘em your card • Use $10 to buy ad, get permanent access to analytics.Twitter.com • 30 day window on follows, unfollows, mentions • 90 days of your tweets (or 500 tweets) .CSV download • ID • Time sent • Faves • Retweets • Replies • Text
  27. 27. Twitter Data: Give ‘em your card • Use $10 to buy ad, get permanent access to analytics.Twitter.com • 30 day window on follows, unfollows, mentions • 90 days of your tweets (or 500 tweets) .CSV download • “Request your archive” • Detailed data on all the tweets sent from your account • No engagement insights, only text, time, etc.
  28. 28. Twitter Data: Followers, following? • Twitsprout.com! Free for three Twitter accounts • Data begins from sign-up date: • Total tweets sent • # of followers • # following • Export as .CSV!
  29. 29. Twitter Data: Keyword-based collection • Hootsuite Archives • Best deal: 10,000 total tweet archive • Can delete archives and restart at any point • (effectively limitless for low-medium rate keywords) • Cons: Needs some massaging to work in Excel • Contains data on: • Tweet text • Sending username • Time sent • Language • Tweet ID (link to tweet)
  30. 30. Twitter Data: Keyword-based collection • Hootsuite Archives • Step 1: Export & convert to Google spreadsheet format
  31. 31. Twitter Data: Keyword-based collection • Hootsuite Archives • Step 1: Export & convert to Google spreadsheet format • Step 2: Download as an Excel file (or .CSV)
  32. 32. Twitter Data: Keyword-based collection • Hootsuite Archives: Basic Manipulations • Rebuilding the link to a single tweet • =CONCATENATE("http://twitter.com/",C2,"/status/",D2) • C column = “from user” • D column = “id”
  33. 33. Twitter Data: Keyword-based collection • Hootsuite Archives: Basic Manipulations • Building a master archive with overlapping keywords • Step 1: Concatenate the text and the sending user • Prevents loss of multiple RTs • =CONCATENATE("@",C2,":"," ",A2) • Produces: Username: Tweet text • Step 2: Use Excel’s ‘Remove Duplicates’ Function
  34. 34. Twitter Data: Keyword-based collection • Hootsuite Archives: Basic Manipulations • Given time in two formats: text & Unix time • Unix time: Seconds since Jan 1st, 1970, excluding leap seconds (easier to use for math) • Convert Unix to Excel time (Serial date) using this formula • Column M contains Unix time • =(M2/86400)+25569+(-5/24) • Will look like: 41353.0116 • Format Cells for ‘Date’, = March 20, 2013
  35. 35. Twitter Data: Keyword-based collection • Hootsuite Archives: Basic Manipulations • Counting number of tweets per day • Assume tweet dates in serial format are Column M • Create column with desired date range in serial format (N) • =COUNTIFS(M:M, ">" & N2, M:M, "<" & N3)
  36. 36. Twitter Visualization: Netlytic.org • Free software created by an academic lab • Creates visualizations in Gephi with no coding knowledge needed
  37. 37. Twitter Visualization: Netlytic.org • Convert Excel file back to .CSV & upload to Netlytic.org • Will not need to ‘Clean data’
  38. 38. Twitter Visualization: Netlytic.org • Select field containing tweet text only • Do not use concatenated username + tweet text
  39. 39. Twitter Visualization: Netlytic.org • Get rid of keywords you do not want for text analysis
  40. 40. Twitter Visualization: Netlytic.org • Keywords visualized by usage over time
  41. 41. Twitter Visualization: Netlytic.org • Proceed to mention network analysis • Ignore chain network analysis
  42. 42. Twitter Visualization: Netlytic.org • Also interactive visualization for more styling
  43. 43. AdrianEbsary.com

×