twitter and facebook data mining solutions
Post on 15-Jul-2015
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Fueling the Data Drive: Facebook & Twitter Data Mining Solutions for Digital Communicators
Adrian J. Ebsary
@AJEbsary
The Content Marketing Process
Starting your archives
• Never trust your data to a third party
• Own your own Excel (.CSV) files
• Don’t settle for time-slice dashboards
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
Excel with Excel
• Plenty of free resources to learn• Google: “learn how to use Excel”
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
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
Data by network by ease-of-access
• Google+• Integrate Google+ Pages into Google Analytics
Data by network by ease-of-access
• Twitter (Tweets by Keyword)• Build your own app or…
Data by network by ease-of-access
• Money, money, money• dev.twitter.com/programs/twitter-certified-products
• More options, more networks, more $$$
Data by network by ease-of-access
• Twitter (Account interaction data)• Want Twitter data? Buy a $10 ad.
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
Facebook Data
Facebook: NewsFeed Algorithm
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
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
Facebook Insights Dashboard: Like Spikes
• Watch for like spikes and identify source• Correlate with posts or events for additional insights
Facebook Insights Dashboard:Like Spikes
• Watch for like spikes and identify source• Correlate with posts or events for additional insights
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
Facebook Page-Specific Data: Virality
• Virality = Engaged users / Total Reach * 100%• More accurate: Total ORGANIC Reach
• Paid reach has less impact on affinity score
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%
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
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/
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
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
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
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.
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!
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)
Twitter Data: Keyword-based collection
• Hootsuite Archives• Step 1: Export & convert to Google spreadsheet format
Twitter Data: Keyword-based collection
• Hootsuite Archives• Step 1: Export & convert to Google spreadsheet format
• Step 2: Download as an Excel file (or .CSV)
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”
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
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
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)
Twitter Visualization: Netlytic.org
• Free software created by an academic lab• Creates visualizations in Gephi with no coding knowledge needed
Twitter Visualization: Netlytic.org
• Convert Excel file back to .CSV & upload to Netlytic.org• Will not need to ‘Clean data’
Twitter Visualization: Netlytic.org
• Select field containing tweet text only• Do not use concatenated username + tweet text
Twitter Visualization: Netlytic.org
• Get rid of keywords you do not want for text analysis
Twitter Visualization: Netlytic.org
• Keywords visualized by usage over time
Twitter Visualization: Netlytic.org
• Proceed to mention network analysis• Ignore chain network analysis
Twitter Visualization: Netlytic.org
• Also interactive visualization for more styling
AdrianEbsary.com
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