GVU Brown Bag Seminar, Georgia Tech 12/6 - GVU Brown Bag Seminar, Georgia Tech 12/6 . emotion (Golder

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  • GVU Brown Bag Seminar, Georgia Tech 12/6

  • emotion (Golder & Macy, 2011; De Choudhury et al,



    diffusion (Kempe, Kleinberg, 2003; Sun, Marlow,

    2009; De Choudhury et al.



    social roles (Agarwal et al, 2008; Naaman et al, 2010)

    network structure (Leskovec et al 2010)

    crisis management (Starbird et al 2010)

    social influence (Bakshy et al, 2011)

    social ties (Gilbert & Karahalios, 2009)




    groups and

    communities (Kumar et al, 2006; Burke

    et al, 2009; De

    Choudhury et al. 2010)

    citizen journalism (Diakopoulos & Shamma, 2010;

    De Choudhury et al 2012)

    urban informatics (Crandall et al, 2009; Cranshaw

    et al, 2012)

  • what I have been doing

    human behavior

    online o how people spread


    o how people form groups

    o how shared social

    artifacts evolve

    o how people express


    o mining frameworks

    for deciphering

    human behavior

    o predictive models

    o analytic insights

    from big data

    o improve the

    experience of the


    o understand the

    impact of these

    technologies on

    their behavior

  • understanding individual behavior

  • Share the joy!


  • It is getting harder to hide pain


  • examine patterns of activity and

    emotional correlates for childbirth

    and postnatal course leveraging

    activity in social media

    (De Choudhury, Counts, Horvitz, CSCW 2013)

  • low-cost, privacy-preserving

    mechanisms to identify new mothers’

    behavior can improve social support

    and encourage postpartum wellness

    (De Choudhury, Counts, Horvitz, CSCW 2013)

  • (1) birth, weigh*, pounds/lbs, inches, length/long,


    (2) announc*, birth of, son/daughter/brother/sister

    (3) announc*, arrival of, son/daughter/brother/sister

    (4) are the parents of, son/daughter/boy/girl/baby

    (5) welcome* home by, brother/sister/sibling*

    (6) is the proud big brother/sister

    (7) after, labor, born

    (8) it’s a boy/girl, born

    (De Choudhury, Counts, Horvitz, CSCW 2013)

  • (De Choudhury, Counts, Horvitz, CSCW 2013)

  • (De Choudhury, Counts, Horvitz, CSCW 2013)

  • (De Choudhury, Counts, Horvitz, CSCW 2013)

  • prenatal-postnatal comparison

  • blue line represents approximate time of childbirth.

    The beige line represents mothers and the green line

    represents the background cohort

    (De Choudhury, Counts, Horvitz, CSCW 2013)

  • Mothers after childbirth (MA)

    [high NA] Ugh, my daughter hates her bassinet. I hate disappointing her. What a miserable day.

    [low activation] My baby is only catnapping during the day. That’s so sad and depressing. I feel


    [low dominance] Anxiety/panic attacks need to eff off!!!!!!!!!!!!!! I’m trying to lead a somewhat

    normal life with my baby!!!! #frustrated #miserable

    [high 1st person pronoun use] No lie I fuckin miss all socializing..... my daughter keeps me

    occupied and exhausted. I have all my moments of the day

    Mothers before childbirth (MP)

    Derek & I sat on our screened in back porch listening to the thunderstorm & rain! So peaceful!

    Just to think in 35 hours we’ll be parents!

    Pregnant for the first time and I’m afraid I won’t be able to stand the labor pain. Husband trying

    to reassure me, but he seems scared too. Thoughts????

    I’m completely thrilled at the prospect of becoming a mother but the weight gain is bothering

    me :(:(. Do I just need to get over myself'? Am I the only one :S

    Days are getting busy!!! Need to start packing for the hospital, in case the baby is coming early!

    (De Choudhury, Counts, Horvitz, CSCW 2013)

  • Microsoft Research

  • (De Choudhury, Counts, Horvitz, CSCW 2013)

  • measure small effect medium effect large effect

    activity 38 29 20

    emotion 17 4 12

    style 43 3 18

    effect sizes (based on Cohen’s d) over the three types of measures.

    Numbers indicate the number of new mothers showing changes following

    childbirth of each effect size.

    (De Choudhury, Counts, Horvitz, CSCW 2013)

  • Mothers w/ small effects

    I know some drs say it’s ok to be on meds while breastfeeding but it kind of freaks me out cause it

    isn't proven longterm for baby's health.

    Days are passing by as I watch my son grow! Can’t wait for more and get together with the daddy!!

    Wish he was here

    Just adjusting to having a new baby, new job and we just moved town. Need to calm down.

    Tips/suggestions on parenting, mothers??

    I'm taking expressed breastmilk from the fridge on outings in the diaper bag and keeping it cool with

    an ice pack. Someone tried it?

    Mothers w/ large effects

    Starting to feel lost. I’m missing my love, my baby. Feel angry n disappointed in myself. Idk what to

    think or do....

    My first time being alone with my baby and I cant stop crying. What is wrong with me? Am I

    depressed? Im just over here balling my eyes out

    My DS doesnt sleep more than 3 hrs at a time and cries often and is so difficult to calm down. Cant

    remember when was the last time I slept

    Feel like having a breakdown! ...like the WORST mother... feel so terribly that this poor child is stuck

    with this horrible monster mother..

    (De Choudhury, Counts, Horvitz, CSCW 2013)

  • size of vocabulary


    percentage of unigrams

    w/ p < .01

    mothers w/ large effects 17,117 27.65%

    mothers w/ small effects 33,785 19.74%

    background cohort 47,214 3.86%

    percent of all unigrams in the language vocabulary used that changed significantly in usage frequency

    after childbirth, i.e. with p < .01 based on paired sample t-tests

    background cohort mothers w/ small effects mothers w/ large effects

    now (↓), shit (↑), back (↑), that (↑), day

    (↓), life (↑), time (↓), them (↑), me (↑),

    you (↑), fuck (↑), today (↓), sleep (↑),

    tonight (↓), love (↓), good (↓), here(↓),

    her (↓), morning (↑), tomorrow (↑), go

    (↑), know (↑), him (↓), people (↓)

    #past (↑), duh (↑), people (↓),

    photo (↑), post (↑), decision (↓),

    reunite (↓), women (↑), story (↑),

    time (↑), asap (↓), do (↑), life (↓),

    wait (↑), fired (↑), days (↑), happy


    haha (↓), blessed (↑), lol (↓),

    #lifecangetbetter (↑), awesome

    (↓), monthly (↑), fantastic (↓),

    cuddle (↑), home (↑), love (↓), sick

    (↑), aww (↑), scary (↑)

    top unigrams showing the most change (in usage frequency) in the postnatal period, compared to the

    prenatal phase

    (De Choudhury, Counts, Horvitz, CSCW 2013)

  • (De Choudhury, Counts, Horvitz, CSCW 2013)

  • predict

  • (De Choudhury, Counts, Horvitz, CSCW 2013)

  • (Corinna & Vapnik, 1995)

    (De Choudhury, Counts, Horvitz, CSCW 2013)

    http://www.cac.science.ru.nl/people/ustun/SVM.JPG http://gaelvaroquaux.github.com/scikit-learn-tutorial/_images/svm_margin1.png

  • Measures C1 C0 Measures C1 C0

    volume -0.878 0.838 verbs -0.911 0.951

    Replies -0.983 1.417 aux-verbs -0.889 0.914

    RT -0.847 2.188 adverbs 3.025 -0.678

    Links -0.791 2.347 preposition -0.899 1.020

    Questions 2.110 -0.749 conjunction -0.890 1.042

    PA -0.798 0.428 negate 2.867 -0.690

    NA 1.726 -0.593 quantifier -0.896 0.950

    activation -0.743 0.525 swear 3.258 -0.751

    dominance -0.717 0.543 tentative 3.710 -0.700

    Followers -0.941 1.451 certain -0.997 1.386

    Followees -0.928 1.013 inhibition 3.425 -0.722


    words 4.574 -0.865 inclusive -0.914 1.048

    1st pp. 1.924 -0.426 exclusive -0.822 2.304

    2nd pp. -0.901 0.872 assent -0.904 1.073

    3rd pp. -0.894 0.990 non-fluency 3.028 -0.724


    pronoun -0.810 2.082 filler 2.929 -0.786

    article 3.940 -0.701

    Median changes in different measures for the two classes of new mothers with

    C1 corresponding to extreme-changing mothers and C0 corresponding to

    mothers showing smaller changes

    (De Choudhury, Counts, Horvitz, CSCW 2013)

  • Measures % Acc. Prec. Recall F1 Spec. AUC

    engagement 71.70% 0.757 0.708 0.727 0.742 0.734

    Mean accuracy of extreme-change mothers (eng) 74.21%

    emotion 71.21% 0.696 0.740 0.714 0.688 0.689

    Mean accuracy o