osn mood tracking: exploring the use of online social ... · we thank dr neal lathia for the use of...
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OSN Mood Tracking: Exploring the Use of Online Social Network Activity as an Indicator of
Mood Changes
James Alexander LeeDr Christos EfstratiouDr Lu Bai
School of Engineering and Digital ArtsUniversity of Kent
United Kingdom
{ jal42, c.efstratiou, l.bai } @kent.ac.uk
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Existing research:
● Long-term studies (months to years)
● Emotional trends of groups
● Single OSN
Psychological State & Online Social Networks
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Analyse the user’s online activity on Facebook and Twitter
Identify features that can be exploited to detect the user’s mood changes
Short time frame (7 day sliding window)
Ground truth data via experience sampling
Aim: Find correlations between mood and online activity
Our Research - OSN Mood Tracking
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Aimed at OSN users who maintain a relatively frequent interaction with Facebook and Twitter
Advertised at a British university(18 - 25 years old)
73 people registered their interest
36 were chosen to participate - self-reported most active online
Recruitment
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Study Duration
Study ran during exam period into summer break
Wider variability of mood changes: exam pressure vs. relaxed summer break
Expected participation: 30 days
Average participation: 28 days
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● Statuses● Posts by friends● Shares● Likes● Comments
Data Collection - Online
Two crawlers developed to collect activity data from the personal timelines and home feeds on Facebook and Twitter every 15 mins
● Tweets● Replies● Retweets
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Data Collection - Ground Truth
Participants installed the smartphone applications Easy M for Android or PACO for iOS
Daily prompts at 10pm to answer two questions:
1. How was your mood today, for the whole day in general?
2. How do you currently feel right now?
Overall response rate: 88%
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Following data collection, both datasets were cleaned
● User reported multiple moods in a single day - later time was used
● Participants were removed completely if:○ The same mood was reported every day○ Final dataset was less than 15 days long
Data Cleaning
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16 participants
406 days of individual data (avg. 25 days per participant)
1,760 online actions (posts, likes, etc.) performed by the participants
Final Dataset
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Methodology
● Which online features best represent mood?
● Normalise mood across participants using z-score
● Extracted online features calculated over 7d sliding window, 6d overlap
● Pearson’s correlation between each online activity feature and each participants’ mood changes
● % of participants with significant correlations with that feature (p < 0.05)
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Statistical Features
Counts of online actions:
● Status updates● Likes● Comments● Posted links / photos / videos● Tweets● Retweets● Hashtags (#)● Mentions (@)
● Character length of statuses / tweets● Activity during morning / afternoon / evening / night
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Statistical Features
Aggregate features:
● Total Facebook activity● Total Twitter activity● Total online activity● Active activities
○ Posts○ Comments○ Tweets○ Replies
● Passive activities○ Likes○ Retweets
● Sentiment analysis
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Results
Total Online Activity
61% of participants demonstrating statistically significant correlation with mood (p < 0.05)
Count of all actions on both Facebook and Twitter
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Participant 1: Positive
Coefficient: 0.45P-value: 0.03
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Participant 2: Negative
Coefficient: -0.46P-value: 0.01
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Participant 3: Weak
Coefficient: 0.09P-value: 0.60
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Mood Tracking System
1. Strong vs. weak classifier (correlation coefficient)
2. Positive vs. negative classifier (signage of coefficient)
3. Total Online Activity feature
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Mood Tracking System
1. The user’s activity on Facebook and Twitter is passively tracked
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Mood Tracking System
2. The user’s mood is classified as predictable or unpredictable
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Mood Tracking System
3. The user’s mood is classified as having a positive or negative correlation with online activity
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Mood Tracking System
4. User is now classified as positive or negative - we can now use this grouping to infer the user’s mood by simply observing their online activity
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Feature Selection for Classifier
● Select a minimum set of features that maximised performance of classifier
● Hill climbing iterative approach
● Features:○ Average length of the Facebook posts (lengthFAvg)○ Average length of the Twitter posts (lengthTAvg)○ Ratio of “active” actions over “passive” actions (activePassiveRatio)○ Ratio of Twitter actions over Facebook actions (twitterFacebookRatio)
● Features capture the level of commitment when interacting with the OSNs
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Strong vs. Weak
● Classifier: Random Forest
● Precision: 95.2%
● Recall: 94.7%
● F1 score: 0.947
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Positive vs. Negative
● Classifier: Voted Perceptron
● Precision: 84.4%
● Recall: 80.0%
● F1 score: 0.763
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Conclusions
First case of exploring correlations between activities over multiple OSNs and real-world mood data captured through experience sampling
Shown it is feasible to track user’s mood changes by analysing their online activity
Can we track our friends’ mood too?
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THANK YOU
Acknowledgements:We thank Dr Neal Lathia for the use of EasyM, Professor Roger Giner-Sorolla, Ana Carla Crispim and Ben Tappin from the School of Psychology, University of Kent for their support and the participants who provided the data.
James Alexander [email protected]