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Predicting what gets ‘Likes’ on Facebook: case study of BlogTO
- 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. 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
- 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 Slides http://bit.ly/rublogto
- 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. - 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. 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. Daily Posting Frequency BlogTO Dataset April 3 – May 3 2017 11,785 Unique Posters 17,748 Posts + Replies @SMLabTO 8
- 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. 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. 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. 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. 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. 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. 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. 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. LIWC Categories Sample Post post Toronto looking like a Unicorn Frappuccino - Photo by alexandramack22 @SMLabTO 17
- 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. 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. 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. 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. Example of Facebook Post with Video @SMLabTO 22
- 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. 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. 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. 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. 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. 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 Slides http://bit.ly/rublogto
- 29. @SMLabTO 29
Notas do Editor
- Dataset Name: Blogto Page (Apr 3 - May 3, 2017) Dataset Last Updated: 2017-05-03 14:27:52 Dataset Source: Facebook Total Messages: 641 BlogTO
- Can we predict what kinds of BlogTO posts get more likes on Facebook? What types of posted content (videos, photos, links, status updates, events) shared on Facebook get more likes? What linguistic and social cues make BlogTO content post more ‘likable’? *** (Are there linguistic strategies that) RQ Why do some content posts get more likes then others How can blogto increase their likes? What content should they be posting (visual aspect) Do we use ‘predict’ in the RQs “can we predict the kinds of post/content gets more likes on facebook?
- Smart art to show progression LIWC – Linguistic Inquiry and Word Count SPSS – Statistical Package for the Social Sciences We will briefly explain how we collected our BlogTO dataset Beginning with Netlytic ** I have bolded each method in red, however if you want to make the bold appear you can change that too.
- Dataset Name:Blogto Page (Apr 3 - May 3, 2017)Dataset Last Updated:2017-05-03 23:27:25Dataset Source:facebookTotal Messages:17748Unique Posters:11785
- Dataset Name:Blogto Page (Apr 3 - May 3, 2017)Dataset Last Updated:2017-05-03 23:27:25Dataset Source:facebookTotal Messages:17748Unique Posters:11785
- Dataset Name:Blogto Page (Apr 3 - May 3, 2017)Dataset Last Updated:2017-05-03 23:27:25Dataset Source:facebookTotal Messages:17748Unique Posters:11785
- raw FB data from BlogTo - to comb through and categorize content from 641 BlogTO Facebook Posts We used Linguistic Inquiry and Word Count software = ‘LUKE’ LIWC Automatic text analysis
- Linguistic cues impact number of fb likes Online behaviours and engagement Uncover strategies and tactics for local news sites
- We chose LIWC bc: Software measures and studies emotional, cognitive, structural components in text Mirror human behavior and speech (well tested) 4th iteration (Pennebaker Texas) Extensive dictionary (structure/content) READ 90 Examples of categories (diversity) Risk – words expressing danger or doubt Power – words signifying superiority, strong, authority or even a bully Leisure – activities like to cook, to chat, going to a movie How does LIWC work and what do these categories mean? ---- **now LIWC dictionary has also been translated into other languages Advantages: It is fast Easy to interpet Has been validated through multiple iterations and uses across different disciplines The output can be easily transferred to statistical softwares
- Left Netlytic Data import into Right LIWC software Posts and see categories on top EXAMPLE COMING UP
- Words highlighted in red belong to one of LIWC categories. Specifically, [review the table] LIWC automatically combs through text Each word is categorized (looking and photo)
- Post text is counted and given a % score We are interested in this % used as analysis To predict FB likes
- Variable Output from LIWC was then Transferred into SPSS (Statistical Package for the Social Sciences) LIWC Dictionary Categories = Independent Variables Number of FB Likes = Dependent Variable Can these variables predict facebook likes? Each of the LIWC categories have the ability to be a predictor of the dependent variable (in our case facebook likes) All possible subsets – provides best subsets after evaluating all possible regression models
- Also type of content (video, picture, link etc) LIWC dictionary categories as variables EX: comparisons, discrepancy
- Take output from LIWC and use SPSS to analyze and predict factors that will impact BlogTO Facebook Likes: 10 of 90 (statistically sign. = results not by random chance) VIDEO Variables 8x stronger in predicting + like R-sq Our model explains 24% of the variance in the dependent variable (#FB likes) Meaning About 76% of the variance is not being accounted for Happy to discuss other variables for future study (predicting human behavior is challenging, literature psychology field r-sq values lower than 50%) We will share our stronger predictors and notable anomalies - any field that attempts to predict human behavior, such as psychology, typically has R-squared values lower than 50%. Humans are simply harder to predict than, say, physical processes. --- ***for this presentation we see a positive and negative relationship with 10 key variables We will only discuss the most notable findings Significance level: we can with confidence say there is a linear relationship between these (independent) variables and BlogTOs Facebook like count in this sample set. Coefficient tells us the variance between these variables and Facebook like count (how much dv will change per standard deviation increase in the predictor v). Coefficient is a constant by which a variable is multiplied (FB likes) Question – which variables matter/influence/predict/are significant?
- Our analysis shows that posts created by BlogTO with video content got more likes compared to any other type of post (photos, links, events, status updates). A very strong predictor (8x stronger then the next variable) This is surprising when considering that video content makes roughly 6% of all BlogTO posts analyzed. We also note that most of the videos relate to food, are people clicking with their appetites? Attract more views and likes through visualizations and glorifications of food in the city Food porn https://www.theguardian.com/lifeandstyle/2017/mar/19/science-of-food-porn-gastrophysics-alluring-food-imagery-psychology Compare link, posts, pictures, video (TOTAL is 641) Video – food content (38) 5.9% of content Photos – sunsets and downtown Toronto (145) 22.6% Links – headlines, information (439) 68.4% Event – (11) 1.7% Status – (7) 1% Tweets with pictures have been shown to get higher levels of engagement http://www.socialmediaexaminer.com/photos-generate-engagement-research/
- Social - social processes category predicts increase in # of FB likes Not surprising bc category suggest human interaction (talking, sharing). words from this category include friends, family and humans interactions matter for most people READ: hey girl….examples So: talk, us, friend, pal, buddy, coworker, mom, brother, cousin, boy, woman, group Extra sample posts: Hello playoffs! Game on Mark your calendars
- Informal – informal language category predicts increase in # of FB likes Not surprising because Internet user-languages and online messages in social media: flexible and informal, with abbreviations and slang: Swearing (stfu) , netspeak (lol, idk, smhh), assent (agree, yes), nonfluencers (er, hmm, umm), fillers (Imean, youknow). READ: yes!….examples (punctuation) Audience? Jargon? Cultural references? New types of strategies for online news groups, Content posts become more sticky through particular discourse, lexicon, cultural references, jargon http://sproutsocial.com/insights/social-media-slang/ Who is your audience? What/who is the ideal audience for BlogTo **our regression showed that netspeak is negative relation, which is surprising, however it was a very small subset of the data So in this case, assent nonfluencers and fillers predict thumbs up Extra sample posts: Awww Hmmm
- Part of subcategory of informal language Surprising given that informal language overall has a positive result Netspeak includes : ), #, @ Do people skim over There is a fine balance in the new digital media age Context? Doesn’t catch attention? Interesting because overall the informal language was a positive This result could be just be a result of a month sample. And also speaks to the nuances in online text and informal language
- For local news sites Conversations need to be engaging Not just shares, need to be recipricol (social processes result) Engage with video Pictures are outdated and static Be smart with netspeak – nuances Direct to activities (MUST see)
- Future work could examine 1 – different content 2 – audiences 3 – timelines and seasons (niche activities)
- Significance level: we can with confidence say there is a linear relationship between these (independent) variables and BlogTOs Facebook like count in this sample set. Coefficient tells us the variance between these variables and Facebook like count (how much dv will change per standard deviation increase in the predictor v). Coefficient is a constant by which a variable is multiplied (FB likes) Take output from LIWC and use SPSS to analyze and predict factors that will impact BlogTO Facebook Likes: To show which LIWC variables influence likes on BlogTOs facebook Which variables might predict ‘sticky’ likeable posts? (we focus on the stronger predictors and notable anomalies) --- ***for this presentation we see a positive and negative relationship with 10 key variables We will only discuss the most notable findings ) Question – which variables matter/influence/predict/are significant?
- SO FOR EXAMPLE (read Cried)…3 category vs Software goes through the text in the document, and runs the text (cried) against the dictionary categories One target at a time, a word is matched for one or more dictionary match the word ‘cried’ is part of different categories (content and structure) (negative emotion, verbs, past focus (time orientation)) each subcategory score will be incremented accordingly --- We did this for our blogto dataset …. 92 Categories as 92 predictor variables to find what content ‘sticks’ and to try and predict what readers are more likely to give a thumbs up If media organizations want to create FB content that gains traction and sticks, there are certain linguistic strategies to follow (video, informal, social) (the ‘here’s what you need to do’) Here is how we arrived to this conclusion (‘how did we get here’)….to SPSS (statistical package for social sciences)
- Affiliation – affiliation (psychological processes, drives to others. That content created with high affiliation gets less Facebook likes is understandable when looking at the posts. The content is particular, and speaks to niche events that will attract the interest from a smaller group of BlogTO followers. So for example, not everyone is an animal lover, cares about falafels or enjoys movies. The topics of the content being posted are issue or agenda specific, rendering less likes. Extra sample posts: 420 celebrations and more Raptors take Game 2 Star Wars Day celebrations and beyond **some of the words in this category are also found in social processes, however I would theorize that it’s the actual content of said posts that makes the difference SLIDE 13-18 are selected either based on being statistically significant (see slide 12, we may want to change the order as right now it is divided by + and – for easy understanding) The rest of the examples are significant but we do have to cut down the talk, and these points might help the conclusion to be strong and concise (structure/content))
- Compare – comparisons (other grammar) – example: greater, best, after. Comparing one entity with another (more than, like, newest, all over all again) Surprising, but can be explained by positivity and uplifting content in the post. Something new/novel to check out or appreciate in the city, which is promised to be better then before or newly improved? Extra sample posts: It\'s bigger than you think This summer keeps on getting better and better It doesn\'t get much cuter than this Slide 19-20 similar to me, we have better results to showcase
- In psychological processes subpart of cognitive processes category Again we see a directive to future, and also advisory (you should look at this, or this will be good for you) Extra sample posts: Wishing this weekend would never end – Photo by Jodh Mankz Big changes could be on the way You\’re going to what to enter this Slide 19-20 similar to me, we have better results to showcase
- Interrog – interrogatives (other grammar) – example: how, when, what, where, how etc. This category is bundled by the who/what/why text, so there isn’t a clear psychological or linguistic focus. Topics range from negative (what a nightmare) about traffic, ‘where is the best Korean food’, ****I suggest it be removed because we have stronger results to present with the given short time. (LIWC limitations) Extra sample posts: You know we\'re in the playoffs when... This Toronto bar knows how to do nachos
- I selected this to start with because it is expected, and differs from the rest of the results. Function – function words (in linguistic dimensions) structure WORDS (expected result) Function words – pronouns, prepositions, articles, conjunctions and auxiliary verbs. FIT AROUND CONTENT (I, the, and, to, a, of, that, in, it, my, is, you, was, for, have, with, he, me, on, but) This isn’t surprising, function words provide the structure to text (not the content, the bones to the meat). These semantic components of language do not carry content or meaning, but serve a function in forming sentences and arranging meaning. Pennebaker (creator of LWIC) 2013 – 55% of the average person’s informal communication, both written and oral, is comprised of the same 200 commonly used function words. Online – offline human interaction mirrors*** Extra sample posts: This should be fun This Toronto bar is not like the others