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Predicting what gets ‘Likes’ on Facebook:
A case study of BlogTO
Philip Mai (@phmai)
Director, Business & Communications
S...
About BlogTO
“Toronto's source for local
news and culture,
restaurant reviews, event
listings and the best of
the city.”
F...
About the
@SMLabTO 3
Research Questions
• What kinds of BlogTO posts get more likes on Facebook?
1. Are there certain types of BlogTO content (...
Methodological Toolkit
Data
Collection:
Netlytic
• Social media data collector
• Text and network analyzer
Text
Analysis:
...
- a social media analytics platform
designed for researchers to
collect, analyze and visualize
publicly available data fro...
Visualize & analyze social networks
Discover popular topicsCollect data from social media
Find & explore emerging
themes o...
Daily Posting Frequency
BlogTO Dataset
April 3 – May 3 2017
11,785 Unique Posters
17,748 Posts +
Replies
@SMLabTO 8
Sample Facebook Data viewed in Excel
pubdate author post type like_count
4/24/2017
17:46:00
blogTO Toronto looking like a ...
BlogTO
Dataset
April 3 – May 3 2017
11,785 Unique Posters
17,748 Posts +
Replies
“Who Replies To Whom” Network
(no recipro...
BlogTO Dataset
April 3 – May 3 2017
11,785 Unique Posters
17,748 Posts +
Replies
641 BlogTO
Posts Focus of
Text Analysis
@...
Collected Data & Metadata as Captured by Netlytic
Sample Post
post Toronto looking like a
Unicorn Frappuccino -
Photo by a...
Methodological Toolkit
Data
Collection:
Netlytic
• Social media data collector
• Text and network analyzer
Text
Analysis:
...
The Power of Words
A lot has been written about the power of words to drive
change, acquire customers, and persuade crowds...
Linguistic Inquiry and Word Count (LIWC)
LIWC Dictionary Contains 90 Word Categories
Such As:
• Linguistic dimensions (art...
Text Analysis with LIWC
BlogTO Facebook Posts collected by
Netlytic
LIWC Output: posts & corresponding scores
for 90 categ...
LIWC Categories
Sample Post
post Toronto looking like a Unicorn
Frappuccino - Photo by
alexandramack22
@SMLabTO 17
LIWC Categories
Sample Post
post Toronto looking like a Unicorn
Frappuccino - Photo by
alexandramack22
LIWC Category Count...
Methodological Toolkit
Data
Collection:
Netlytic
• Social media data collector
• Text and network analyzer
Text
Analysis:
...
Using SPSS to Predict BlogTO Facebook Likes
We tested post type( videos, photo, link, etc…)+ 90 LIWC dictionary
categories...
Result: Using SPSS to Predict BlogTO Facebook Likes
10 (of 90) LIWC Dictionary Categories were found to be Statistically
S...
Example of Facebook Post with Video
@SMLabTO 22
Example of Facebook Post with High
‘Social Processes’ (ex: buddy, coworker, mom)
Additional sample FB posts
in this catego...
Example of Facebook Post with High
‘Informal Language’ (ex: OK, ummm, blah)
Additional sample FB posts
in this category
1....
Example of Facebook Post with High
‘Netspeak’ (ex: thx, btw, brb)
Additional sample FB posts
in this category
1. “Yup”
2. ...
Implications
Be …
Engage with your audience through posts & replies (not just shares)
Other studies showed that it’ll help...
Future Research
1
Analyze the content
of photos, videos
and blogs shared on
Facebook (not just
Facebook textual
posts)
2
A...
Predicting what gets ‘Likes’ on Facebook:
A case study of BlogTO
Philip Mai (@phmai)
Director, Business & Communications
S...
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Predicting what gets ‘Likes’ on Facebook: case study of BlogTO

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Social media is now the place where people are gathering en masse to discuss the news with their friends, neighbors and complete strangers. This change in news consumers’ behavior is proving to be a challenge for local news, but it is also an opportunity. Users and system generated data from social media can also be a boon for content creators. This presentation will feature a case study showing how publishers can use social media analytics to gain insights into their audience and how to use this information to foster a stronger sense of community around their brand of journalism. The case study will focus on how to use Netlytic, a cloud-based social media analytics tool, to mine the public Facebook interactions of the readers of BlogTO, a regional, Canadian-based media outlet, to find out what their readers are interested in and what engages them.

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Predicting what gets ‘Likes’ on Facebook: case study of BlogTO

  1. 1. Predicting what gets ‘Likes’ on Facebook: A case study of BlogTO Philip Mai (@phmai) Director, Business & Communications Social Media Lab Ryerson University Priya Kumar (@link_priya) Postdoctoral Fellow Social Media Lab Ryerson University
  2. 2. About BlogTO “Toronto's source for local news and culture, restaurant reviews, event listings and the best of the city.” Facebook Page (est. in 2004) ~300K Followers @SMLabTO 2
  3. 3. About the @SMLabTO 3
  4. 4. Research Questions • What kinds of BlogTO posts get more likes on Facebook? 1. Are there certain types of BlogTO content (videos, photos, links, events, status updates) that are more engaging for readers? 2. Are there linguistic cues that can predict the ‘likeability’ of BlogTO’s posted content on Facebook? @SMLabTO 4
  5. 5. Methodological Toolkit Data Collection: Netlytic • Social media data collector • Text and network analyzer Text Analysis: LIWC • Automated text analysis (LIWC – Linguistic Inquiry & Word Count ) Statistical Analysis: SPSS • Statistical software @SMLabTO 5
  6. 6. - a social media analytics platform designed for researchers to collect, analyze and visualize publicly available data from… • Twitter, Youtube, Instagram, Facebook, blogs, etc… • Used by thousands of students & scholars Netlytic.org @SMLabTO 6
  7. 7. Visualize & analyze social networks Discover popular topicsCollect data from social media Find & explore emerging themes of discussions @SMLabTO 7 Data Collection & Analysis Netlytic.org
  8. 8. Daily Posting Frequency BlogTO Dataset April 3 – May 3 2017 11,785 Unique Posters 17,748 Posts + Replies @SMLabTO 8
  9. 9. Sample Facebook Data viewed in Excel pubdate author post type like_count 4/24/2017 17:46:00 blogTO Toronto looking like a Unicorn Frappuccino - Photo by alexandramack22 photo 6431 4/4/2017 18:31:00 blogTO Some of Toronto's favourite food vendors are now in one place video 5701 4/7/2017 18:31:00 blogTO Toronto has a new spot for epic ice cream treats video 5225 4/10/2017 14:16:41 blogTO Mark your calendars link 4894 4/15/2017 18:31:00 blogTO Toronto just got a secret superhero and villain themed restaurant video 4378 4/8/2017 8:31:00 blogTO Good morning! - Photo by zzoomed photo 4066 @SMLabTO 9
  10. 10. BlogTO Dataset April 3 – May 3 2017 11,785 Unique Posters 17,748 Posts + Replies “Who Replies To Whom” Network (no reciprocal ties) @SMLabTO 10 Data Collection Netlytic.org
  11. 11. BlogTO Dataset April 3 – May 3 2017 11,785 Unique Posters 17,748 Posts + Replies 641 BlogTO Posts Focus of Text Analysis @SMLabTO 11
  12. 12. Collected Data & Metadata as Captured by Netlytic Sample Post post Toronto looking like a Unicorn Frappuccino - Photo by alexandramack22 date 4/24/2017 17:46:00 author blogTO type photo like_count 6431 link https://www.facebook.com/blogt o/posts/10154396883870009 @SMLabTO 12
  13. 13. Methodological Toolkit Data Collection: Netlytic • Social media data collector • Text and network analyzer Text Analysis: LIWC • Automated text analysis (LIWC – Linguistic Inquiry & Word Count ) Statistical Analysis: SPSS • Statistical software @SMLabTO 13
  14. 14. The Power of Words A lot has been written about the power of words to drive change, acquire customers, and persuade crowds … What is the role of language in driving engagement and traffic to local news sites? @SMLabTO 14
  15. 15. Linguistic Inquiry and Word Count (LIWC) LIWC Dictionary Contains 90 Word Categories Such As: • Linguistic dimensions (articles, verbs) • Psychological constructs (affect, cognition) • Personal concerns (work, leisure) • Informal language (swear words) online speech • Punctuation (periods commas) LIWC Sample LIWC Categories (90 in total) • “Risk” - words that are perceived as threatening • “Power” - words that signify strength or control • “Leisure” - words that refer to activities not associated with work @SMLabTO 15
  16. 16. Text Analysis with LIWC BlogTO Facebook Posts collected by Netlytic LIWC Output: posts & corresponding scores for 90 categories post type Toronto looking like a Unicorn Frappuccino - Photo by alexandramack22 photo Some of Toronto's favourite food vendors are now in one place video Toronto has a new spot for epic ice cream treats video Mark your calendars link Toronto just got a secret superhero and villain themed restaurant video Good morning! - Photo by zzoomed photo @SMLabTO 16
  17. 17. LIWC Categories Sample Post post Toronto looking like a Unicorn Frappuccino - Photo by alexandramack22 @SMLabTO 17
  18. 18. LIWC Categories Sample Post post Toronto looking like a Unicorn Frappuccino - Photo by alexandramack22 LIWC Category Count Score % # of Words 9 100% Function 3 33% Prep 2 22% Percept 2 22% See 2 22% Article 1 11% Verb 1 11% Compare 1 11% Used in the analysis @SMLabTO 18
  19. 19. Methodological Toolkit Data Collection: Netlytic • Social media data collector • Text and network analyzer Text Analysis: LIWC • Automated text analysis (LIWC – Linguistic Inquiry & Word Count ) Statistical Analysis: SPSS • Statistical software @SMLabTO 19
  20. 20. Using SPSS to Predict BlogTO Facebook Likes We tested post type( videos, photo, link, etc…)+ 90 LIWC dictionary categories as possible predictors of #Likes FB Like Count Post Type Social Processes Informal Language Function Words Comparisons Discrepancy Analytic Thinking Affiliation Interrogatives Netspeak buddy, coworker, mom … video, photo, link, event, share OK, ummm, blah … pronouns, prepositions, articles … greater, best, more than … should, would, could … logical and hierarchical thinking e.g., buddy, coworker, mom, brother… how, when, what, where, why … thx, btw, brb… @SMLabTO 20
  21. 21. Result: Using SPSS to Predict BlogTO Facebook Likes 10 (of 90) LIWC Dictionary Categories were found to be Statistically Significant (p<.05) FB Like Count Post Type = Video (8x more influential than the next category) Social Processes Informal Language Function Words Comparisons Discrepancy Analytic Thinking Affiliation Interrogatives Netspeak R2 = 0.24 @SMLabTO 21
  22. 22. Example of Facebook Post with Video @SMLabTO 22
  23. 23. Example of Facebook Post with High ‘Social Processes’ (ex: buddy, coworker, mom) Additional sample FB posts in this category 1. “Show mom some love” 2. “Attention parents!” 3. “For your next date night” @SMLabTO 23
  24. 24. Example of Facebook Post with High ‘Informal Language’ (ex: OK, ummm, blah) Additional sample FB posts in this category 1. “Yes, yes, yes!” 2. “Try ‘em all” 3. “FYI” @SMLabTO 24
  25. 25. Example of Facebook Post with High ‘Netspeak’ (ex: thx, btw, brb) Additional sample FB posts in this category 1. “Yup” 2. “Hmmm” 3. “Awww” @SMLabTO 25
  26. 26. Implications Be … Engage with your audience through posts & replies (not just shares) Other studies showed that it’ll help to build a community and not just attract followers Conversational Use more videos! Photos are so last year?Visual Embrace informal language but avoid netspeak;Informal Use posts that project to future or direct to activity e.g., “this should be good”, “this is a must-see” Future- forward @SMLaTO 26
  27. 27. Future Research 1 Analyze the content of photos, videos and blogs shared on Facebook (not just Facebook textual posts) 2 Analyze who is engaging with the posts 3 Account for the temporality and seasonality of the Toronto–scene @SMLabTO 27
  28. 28. Predicting what gets ‘Likes’ on Facebook: A case study of BlogTO Philip Mai (@phmai) Director, Business & Communications Social Media Lab Ryerson University Priya Kumar (@link_priya) Postdoctoral Fellow Social Media Lab Ryerson University

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