Noticing the Nuance: Designing intelligent systems that can understand semantic, psychological, and behavioral dimensions of our digital footprints

NOTICING THE NUANCE: Designing intelligent systems that can understand semantic, psychological, and behavioral dimensions

of our digital footprints

Elizabeth L. Murnane

[email protected] www.cs.cornell.edu/~elm236/

ABOUT ELIZABETH

Currently •  3nd year PhD at Cornell Information Science •  Committee: Profs. Dan Cosley (chair), Claire Cardie, Geri Gay

Research •  Personalization; IR/NLP; Personal Informatics; Affective-‐, Semantic-‐,

Social-‐ Computing •  2011 NSF Graduate Research Fellow

Background •  2007 MIT S.B. in Mathematics with Computer Science •  Co-‐founded MIT CSAIL startup

USER-‐CENTRIC DATA

•  Explicit & Implicit

•  User-‐generated content

•  Sensor data

•  Big Data & Big Personal Data (“Little Data”)

DIGITAL FOOTPRINTS

DIGITAL FOOTPRINTS

•  Search Queries

DIGITAL FOOTPRINTS


•  Social web, microblogs, media sharing

DIGITAL FOOTPRINTS



•  Mobile sensing, personal informatics, life-‐logging, check-‐ins

DIGITAL FOOTPRINTS



•  Mobile sensing, personal informatics, life-‐logging, check-‐ins

•  Social networking

NUANCED DIMENSIONS OF DATA •  Semantics

•  Helping machines extract intended meaning from an individual’s content

•  Personality & Emotion

•  Helping machines interpret psychological, affective, and subjective characteristics of users and their data

•  Behavior

•  Helping machines understand the dynamics of both private and interpersonal activities

APPLICATION AREAS

Knowledge Sharing

Personal Informatics

Information Retrieval

RESEARCH PROJECTS

Information Retrieval Knowledge Sharing Personal Informatics Computational Problem:

Dimensions Mined:

Projects:

•  Semantic •  Psychological

•  Psychological •  Behavioral

•  CeRI •  Outreach •  Task routing •  Commenting

interface

•  Smart Pensieve •  Activity Rhythms •  Smoking Cessation


•  RESLVE •  Sentiment-‐based search

RESEARCH PROJECTS


Dimensions Mined:

Projects:



•  RESLVE •  CeRI •  Outreach •  Task routing •  Commenting

interface



•  Sentiment-‐based search

Information Retrieval Knowledge Sharing Personal Informatics

THE RESLVE PROJECT

•  Gain better understanding of challenges machines face in understanding semantic meaning of social Web data

•  Use those insights to develop more advanced computational methods that can more reliably make sense of this data


SOCIAL WEB


SOCIAL WEB

10 million pages per day


SOCIAL WEB

800 million visitors per month


SOCIAL WEB

7 billion images (twice 4 years ago)


TASK DEFINITION


TASK DEFINITION


Named En)ty Recogni)on (NER) •  SystemaEcally idenEfying menEons of en##es (e.g.,

people, places, concepts, ideas)

TASK DEFINITION

Named En)ty Recogni)on (NER) •  SystemaEcally idenEfying menEons of en##es (e.g.,

people, places, concepts, ideas)

Named En)ty Disambigua)on (NED) •  Resolving the intended meaning of ambiguous enEEes from

mulEple candidate meanings


AMBIGUOUS ENTITIES

aaahh one more day un,l finn!!! #cantwait

office holiday party Beetle


AMBIGUOUS ENTITIES




AMBIGUOUS ENTITIES




AMBIGUOUS ENTITIES




Footage:

office holiday party


Footage:


Footage: • Workplace?


Footage:


Footage: • Workplace? • TV Show?


Footage:



Episode 4


Footage:


Episode 4


• US Version? • UK Version?


Episode 4


office, december 3


• US Version? • UK Version?


ANALYSIS

Data Sample

•  TwiKer: tweets •  YouTube: video Etles, descripEons •  Flickr: photo tags, Etles, descripEons


TEXT LENGTH

•  Longest uKerances sEll shorter than even shortest texts from NER task corpora like Reuters-‐21578, Brown-‐Corpus

0"

5"

10"

15"

20"

25"

30"

10"

40"

70"

100"

130"

160"

190"

300"

450"

600"

800"

1100"

1400"

2500"

4000"

5500"

7000"

8500"

10000"

11500"

13000"

14500"

Twi/er" YouTube" Flickr"Reuters" Brown"


HIGH AMBIGUITY

0"

0.1"

0.2"

0.3"

0.4"

0.5"

0.6"

0.7"

0.8"

0.9"

1"

Wikipedia"Miner" DBPedia"Spotlight"

• NER services have low confidence


HIGH AMBIGUITY

• NER services have low confidence

• Many potenEal candidates (2 to 163, avg. 5-‐6, median 4)

0"

0.1"

0.2"

0.3"

0.4"

0.5"

0.6"

0.7"

0.8"

0.9"

1"

Wikipedia"Miner" DBPedia"Spotlight"


HIGH AMBIGUITY

•  91% of uKerances contain at least 1 ambiguous enEty

•  2/3 of enEEes detected are ambiguous

•  Almost no enEEes without at least 2 senses to disambiguate


CHALLENGES & FOCUS

•  Short Length •  Sparse Lexical Context

•  Noisy •  Highly personal in nature


CHALLENGES & FOCUS

•  Short Length •  Sparse Lexical Context

•  Noisy •  Highly personal in nature


LIMITATIONS OF EXTANT RESEARCH

Tweets severely degrade tradiEonal techniques




•  Stanford NER: F1 drops 90% à 46%

•  DBPedia Spotlight & Wikipedia Miner: P@1 < 40%






Recent strategies






Recent strategies

•  Crowd-‐sourcing •  LimitaEon: Dependent on reliable human workers






Recent strategies

•  Crowd-‐sourcing •  LimitaEon: Dependent on reliable human workers

•  Automated aKempts •  LimitaEon: Focus on NER not NED •  LimitaEon: Generalizability beyond TwiKer?


HYPOTHESES


•  User has core interests •  User more likely to menEon an enEty about a topic relevant to personal interests than menEon a topic of non-‐interest

•  User expresses these interests consistently in content she posts online in mulEple communiEes

•  Can use a semanEc knowledge base to formally represent these topics of interest

HYPOTHESES


•  User has core interests •  User more likely to menEon an enEty about a topic relevant to personal interests than menEon a topic of non-‐interest

•  User expresses these interests consistently in content she posts online in mulEple communiEes

•  Can use a semanEc knowledge base to formally represent these topics of interest

•  Wikipedia •  ArEcles, categories effecEvely represent topic •  CompaEble with NER toolkits (DBPedia Spotlight, Wikipedia Miner)  ArEcle ediEng behavior ≈ interests

QUALITATIVE ANALYSIS: STABLE INTERESTS

User’s topics of contribuEon similar across Web:

On average, 52.4% of enEEes a user menEons in social Web (e.g., “Java”) have at least 1 candidate sense in same parent category of Wikipedia arEcle same user edited (e.g., “Programming language”)

If extend to just 4 parents up category hierarchy, get all 100%



User’s topics of contribuEon similar across Web:

Same Topic

On average, 52.4% of enEEes a user menEons in social Web (e.g., “Java”) have at least 1 candidate sense in same parent category of Wikipedia arEcle same user edited (e.g., “Programming language”)

If extend to just 4 parents up category hierarchy, get all 100%

Ambiguous YouTube post: office, december 3

Same user’s recent Wikipedia edit: <item userid="xxxx" user="xxxx” pageid="31841130” ,tle= "The Office (U.S. season 8)"/>



A user’s topics of contribuEon similar across Web:

Same Topic

Same categories

  On average, 52.4% of enEEes a user menEons in social Web (e.g., “Java”) have at least 1 candidate sense in same parent category of Wikipedia arEcle same user edited (e.g., “Programming language”)

  If extend to just 4 parents up category hierarchy, get all 100%

Ambiguous YouTube post: office, december 3

Same user’s recent Wikipedia edit: <item userid="xxxx" user="xxxx” pageid="31841130” ,tle= "The Office (U.S. season 8)"/>


STRATEGY


Ø  Bridge user idenEty between social Web and knowledge base, K Ø Model interests using K’s organizaEonal scheme Ø  Rank enEty senses according to relevance to interests

EXPLORING A PERSONALIZED SOLUTION

 Individual-‐centric approach to NED




 Incorporates external, user-‐specific semanEc data

Personal Context





 Model personal interests with respect to this informaEon

Personal Context






 Determine user’s likely intended meaning of ambiguous enEty based on similarity between potenEal meanings and interests

Personal Context



 Individual-‐centric approach to NED  Incorporates external, user-‐specific semanEc data


 Determine user’s likely intended meaning of ambiguous enEty based on similarity between potenEal meanings and interests

RESLVE Resolving EnEty Sense by LeVeraging Edits

Personal Context


IMPLEMENTATION: THE RESLVE SYSTEM

RESLVE (Resolving EnEty Sense by LeVeraging Edits) addresses NED by:

pre-processor

Wikipedia Miner

user utterances unstructured short texts

DBPedia Spotlight

top ranked personally-

relevant candidates

entity

mmm

entity

username

user contributed structured documents

user interest model

BRIDGING USER

IDENTITY

MODELING USER

INTEREST

I II

IIIRANKING

CANDIDATES BY PERSONAL RELEVANCE

mmm

m mm m

mmm

entity

entity

detected entities & candidate meanings ("m")




I.  ConnecEng social Web + Wikipedia editor idenEty

pre-processor

Wikipedia Miner


DBPedia Spotlight


relevant candidates

entity

mmm

entity

username


user interest model

BRIDGING USER

IDENTITY

MODELING USER

INTEREST

I II

IIIRANKING


mmm

m mm m

mmm

entity

entity






II.  Modeling topics of interests using arEcle edits

pre-processor

Wikipedia Miner


DBPedia Spotlight


relevant candidates

entity

mmm

entity

username


user interest model

BRIDGING USER

IDENTITY

MODELING USER

INTEREST

I II

IIIRANKING


mmm

m mm m

mmm

entity

entity







III.  Ranking enEty candidates by personal relevance

pre-processor

Wikipedia Miner


DBPedia Spotlight


relevant candidates

entity

mmm

entity

username


user interest model

BRIDGING USER

IDENTITY

MODELING USER

INTEREST

I II

IIIRANKING


mmm

m mm m

mmm

entity

entity







III.  Ranking enEty candidates by personal relevance

pre-processor

Wikipedia Miner


DBPedia Spotlight


relevant candidates

entity

mmm

entity

username


user interest model

BRIDGING USER

IDENTITY

MODELING USER

INTEREST

I II

IIIRANKING


mmm

m mm m

mmm

entity

entity



PHASE 1: BRIDGING WEB IDENTITIES

pre-processor

Wikipedia Miner


DBPedia Spotlight


relevant candidates

entity

mmm

entity

username


user interest model

BRIDGING USER

IDENTITY

MODELING USER

INTEREST

I II

IIIRANKING


mmm

m mm m

mmm

entity

entity



•  Connect idenEty of social media user with Wikipedia editor

PHASE 1: BRIDGING WEB IDENTITIES •  Connect idenEty of social media user with Wikipedia editor

•  Simple string matching   Iofciu, 2011; Perito, 2011

pre-processor

Wikipedia Miner


DBPedia Spotlight


relevant candidates

entity

mmm

entity

username


user interest model

BRIDGING USER

IDENTITY

MODELING USER

INTEREST

I II

IIIRANKING


mmm

m mm m

mmm

entity

entity



pre-processor

Wikipedia Miner


DBPedia Spotlight


relevant candidates

entity

mmm

entity

username


user interest model

BRIDGING USER

IDENTITY

MODELING USER

INTEREST

I II

IIIRANKING


mmm

m mm m

mmm

entity

entity


 Models user’s topics of interest using bridged Wiki account’s ediEng-‐history

 Compares similarity of those topics to topic associated with candidate sense


PHASE 2: REPRESENTING USERS AND ENTITIES



 Content-‐based & knowledge-‐graph based similarity

pre-processor

Wikipedia Miner


DBPedia Spotlight


relevant candidates

entity

mmm

entity

username


user interest model

BRIDGING USER

IDENTITY

MODELING USER

INTEREST

I II

IIIRANKING


mmm

m mm m

mmm

entity

entity




MODELING A KNOWLEDGE CONTEXT

 Knowledge base, K

 K=(N,E)

 2 node types:   Categories   Topics

c1c2

c4

t3t2

c3

d2d1 d3

t1


USER INTEREST MODEL

•  EdiEng a descripEon signals interest in associated topic •  Topic nodes: all topics user edited descripEon of •  Category nodes: categories reachable in knowledge graph from those topics •  Edge weight = inverse of shortest path length

! c1 c2 c3 c4

t1 !!! 1!

!!! 0!

t2 !!! 1!

!!! 1!

t3 0! 0! !!! 1!

•  Same representaEon for candidates




 Content-‐based & knowledge-‐graph based similarity

 Weighted vectors used to represent user and candidate sense

pre-processor

Wikipedia Miner


DBPedia Spotlight


relevant candidates

entity

mmm

entity

username


user interest model

BRIDGING USER

IDENTITY

MODELING USER

INTEREST

I II

IIIRANKING


mmm

m mm m

mmm

entity

entity




PHASE 3: RANKING BY PERSONAL RELEVANCE

Output highest scoring candidate as intended meaning by measuring: sim(u,m)=α*simcontent(u,m)+(1-‐α)*simcategory(u,m)

pre-processor

Wikipedia Miner


DBPedia Spotlight


relevant candidates

entity

mmm

entity

username


user interest model

BRIDGING USER

IDENTITY

MODELING USER

INTEREST

I II

IIIRANKING


mmm

m mm m

mmm

entity

entity



PRE-‐PROCESSING & PREPARATION MODULES

pre-processor

Wikipedia Miner


DBPedia Spotlight


relevant candidates

entity

mmm

entity

username


user interest model

BRIDGING USER

IDENTITY

MODELING USER

INTEREST

I II

IIIRANKING


mmm

m mm m

mmm

entity

entity



pre-processor

Wikipedia Miner


DBPedia Spotlight


relevant candidates

entity

mmm

entity

username


user interest model

BRIDGING USER

IDENTITY

MODELING USER

INTEREST

I II

IIIRANKING


mmm

m mm m

mmm

entity

entity


PRE-‐PROCESSING & PREPARATION MODULES

EXPERIMENT

Labeling correct enEty meaning

•  1545 valid ambiguous enEEes

•  Mechanical Turk CategorizaEon Masters

•  Averaged observed agreement across all coders and items = 0.866

•  Average Fleiss Kappa = 0.803

•  918 unanimously labeled ambiguous enEEes


PERFORMANCE

Metric

•  Precision at rank 1 (P@1)


PERFORMANCE

Metric •  Precision at rank 1 (P@1)

Methods of comparison •  Human annotated gold standard •  RC: Randomly sorted candidates •  PF: Prior frequency •  RU: RESLVE given a random Wikipedia user's interest model •  DS: DBPedia Spotlight • WM: Wikipedia Miner


RESULTS

Flickr YouTube

RESLVE 0.63 0.76 0.84

RC 0.21 0.32 0.31

PF 0.74 0.69 0.66

RU 0.51 0.71 0.78

WM 0.78 0.58 0.80

DS 0.53 0.67 0.63

Twitter


RESULTS

•  Best performance on YouTube texts

(longest) due to content-‐based sim


RESULTS



• Outperforms on more personal text (e.g., tweets)


RESULTS



• Outperforms on more personal text (e.g., tweets)

•  Less effecEve on impersonal text (e.g., photo geo-‐tags) •  High prior frequency so standard methods suffice •  Personally-‐unfamiliar topics so not likely to make Wiki edits about them •  Stable interests assumpEon breaks down here


RESEARCH PROJECTS




•  RESLVE •  CeRI •  Outreach •  Task routing •  Commenting

interface



•  Sentiment-‐based search

Computational Problem:

Dimensions Mined:

Projects:

SENTIMENT BASED SEARCH


SENTIMENT BASED SEARCH

•  Zip codes of 10 most populated cities, 10 least populated cities, 10 random cities across the country

•  54,015 places across 1500 US cities •  Movie theaters, hotels, spas, stores, restaurants, etc.






CHALLENGES FOR USERS

•  Interpreting mixed reviews

•  Confidence in reviewer’s subjective opinions

•  Reading multiple reviews


RESEARCH QUESTIONS

•  Language and rating •  How does a place’s rating relate to the language used in its reviews?

•  Personality and rating •  Do people with similar personalities tend to like or dislike the same places?

•  Search interfaces •  How can we rank search results in order to recommend places according to how appealing their atmosphere is likely to be to a user based on her personality and mood?


STRATEGY

•  Extract features from reviews using Linguistic Inquiry and Word Count (LIWC) and MRC Psycholinguistic Database

•  Support vector models trained by Mairesse algorithm to derive Big Five personality types of reviewers

•  Average personality score of reviewers of a place who rated the place 5 or higher/lower as proxy for people who like/dislike a location’s essence


SEARCH INTERFACES


Extraversion = Red, Stability = Purple, Agreeableness = Green, Conscientiousness = Blue,

Openness = Yellow



CONSUMING INFORMATION


CREATING & SHARING INFORMATION

Knowledge Sharing Personal Informatics Information Retrieval

RESEARCH PROJECTS

Knowledge Sharing Personal Informatics


interface







•  CeRI



Dimensions Mined:

Projects:

CERI: CORNELL E-‐RULEMAKING INITIATIVE

•  Law School

•  Legal Information Institute (LII)

•  Information Science

•  Computer Science


BACKGROUND

•  Rulemaking: process federal agencies use to create regulations (called “rules”)

•  e-‐Rulemaking: the use of digital technologies during this process

•  Regulations.gov, RegulationRoom.org: online communities that allow people to learn about, discuss, and react to proposed rules during e-‐Rulemaking process


BACKGROUND

•  Rulemaking: process federal agencies use to create regulations (called “rules”)

•  e-‐Rulemaking: the use of digital technologies during this process

•  Regulations.gov, RegulationRoom.org: online communities that allow people to learn about, discuss, and react to proposed rules during e-‐Rulemaking process


PARTICIPATION PATTERNS •  Regulations.gov

•  14,000 rules •  2 million comments

•  Regulation Room •  5 live rules •  1,318 comments

•  Common problem: under-‐contribution


PARTICIPATION PATTERNS


PARTICIPATION PATTERNS

Frequency of comments per rule Comments per rule across agencies Knowledge Sharing Personal Informatics Information Retrieval

CHALLENGE


•  A major goal of eRulemaking is to increase public participation across a broad audience and make the process more representative

•  A major challenge is sustained participation by multiple actors across rules

SOLUTION

•  Twitter is a popular medium where people express views and ideas

•  Identify and target Twitter users who may be interested in contributing feedback on a rule

A solution is to bring new users to an e-‐rule

•  A major goal of eRulemaking is to increase public participation across a broad audience and make the process more representative

•  A major challenge is sustained participation by multiple actors across rules


EXPERIMENT •  How useful is Twitter content for drawing inferences about

people’s interests and knowledgeability about a topic?

•  Are users who create content about topics relevant to an e-‐rule more likely to engage in related e-‐Rulemaking processes if targeted with requests for participation?


1 Identify Subjects

Bio Tweet

Combo Control

•  Similarity between query and each document •  Highest score used to assign user to condition

* via Google Keyword Tool, which provides less technical words used by public to discuss same topics

User: Rule: Document term matrix Query

q = words in rule + query expansion *

D1 = bio D2 = tweets D3 = bio+tweets (“combo”)


2

²  Highest ranked users in each group sent an outreach tweet

Send Tweets


3

•  Engagement (retweets, replies, and follows)

•  Click Through Rate

•  Contributed to the rule

Measure Response


PSYCHOLOGICAL TRAITS OF EFFECTIVE CONTRIBUTORS


•  Connecting psychological traits, language use, and contribution capability

•  Classification, Outreach, and Task Routing

PSYCHOLOGICAL TRAITS OF EFFECTIVE CONTRIBUTORS

•  Connecting psychological traits, language use, and contribution capability

•  Classification, Outreach, and Task Routing

•  Inventories •  Self-‐efficacy & self-‐esteem •  Big 5 personality

•  Self-‐regulation & self-‐monitoring •  Trendsetting & Opinion Leadership •  Pro-‐social & altruistic value orientations


COMPUTATIONAL SUPPORTS FOR KNOWLEDGE SHARING

•  Meaningful games to teach community norms

•  Personalized rule recommendation

•  Providing assistance, prompts, and examples to improve the quality of contributions


COMPUTATIONAL SUPPORTS FOR KNOWLEDGE SHARING

•  Meaningful games to teach community norms

•  Personalized rule recommendation

•  Providing assistance, prompts, and examples to improve the quality of contributions


RESEARCH PROJECTS

Knowledge Sharing



interface







Information Retrieval Personal Informatics Information Retrieval Knowledge Sharing


Dimensions Mined:

Projects:

RESEARCH PROJECTS

Knowledge Sharing



interface

•  Activity Rhythms •  Smoking Cessation






•  Smart Pensieve



Dimensions Mined:

Projects:

REMINISCENCE

•  Current tools are too technically focused •  Emphasize data capture and logging (photos, videos,

scanned documents) •  Treats memories as information to be later manipulated


REMINISCENCE

•  Current tools are too technically focused •  Emphasize data capture and logging (photos, videos,

scanned documents) •  Treats memories as information to be later manipulated

•  But the activity of reminiscence is actually.. •  Imprecise •  Social •  Nuanced


SMART PENSIEVE: WHAT MAKES A MEMORY MEANINGFUL? •  Content type

•  Photos, wall posts, status updates, event information

•  Social dynamics •  Tie strength, kind of relationship, amount of interaction

•  Temporal features •  Recent, distant past


TRIGGERING MEMORY


PAM, PANAS, ISS, MSCS


COLLECTING SURVEY DATA

•  Laboratory setting •  Pro: Can monitor participants & ensure data quality •  Con: More time consuming for researcher •  Con: Higher pay rates

•  Online surveys •  Pro: Allow larger scale collection •  Pro: Cheaper (time & money) •  Con: Drop-‐outs and missing responses common



IMPROVING SURVEY ADMINISTRATION

RESEARCH PROJECTS

Knowledge Sharing



interface

•  Smart Pensieve

•  Smoking Cessation






•  Activity Rhythms


Dimensions Mined:

Projects:

BEHAVIOR & HEALTH

•  Assess sleep patterns & circadian rhythm

•  Capture behavioral factors associated with stress

•  Approach •  Screen on/off

•  Unlocking •  Application usage •  Internet search •  SMS, email, phone


BEHAVIOR & HEALTH

•  Scheduling Patterns •  Socially-‐Oriented Behaviors

•  Approach •  Calendar entries, social media posts, messages

•  Psycholinguistic Analysis •  Personality Inventory


RESEARCH PROJECTS

Knowledge Sharing



interface

•  Smart Pensieve •  Activity Rhythms






•  Smoking Cessation


Dimensions Mined:

Projects:

SMOKING CESSATION

•  Leading cause of preventable death & leading form of chemical dependence in U.S.

•  44 million smokers in the U.S. alone (1/5 of population)

•  68.8% report they want to quit and over 50% have tried for at least 1 day in the past year

•  Relapse common & a minority permanently abstain


INTERVENTION •  Requires tailoring to individual conditions

•  Lack of long term patient assessment & follow-‐up

•  Access and affordability are obstacles


INTERVENTION •  Requires tailoring to individual conditions

•  Lack of long term patient assessment & follow-‐up

•  Access and affordability are obstacles

•  Technology based interventions have major shortcomings •  Low adherence to established guidelines •  Not personalized •  Unable to handle user struggles and setbacks


FACTORS INFLUENCING OUTCOME •  Personal, psychological, emotional traits

•  Behaviors & activities

•  Environment and social interactions

•  Cessation motivations and process

LEVERAGING DIGITAL FOOTPRINTS

•  Naturally expressed language

•  Content is posted spontaneously and regularly

•  Social setting •  Low-‐cost, large-‐scale, longitudinal data access


MAKE A PREDICTION  General illness + coughing + wheezing = Today I quit smoking.

 Just saw a cigarette commercial with people with holes in their throat. It's official. No more cigarettes.

 Today, I quit smoking. My son came home with an ashtray he made in arts and crafts class. FML







n  i’m cool, day 4 no cigs but my mom smokes, i stay with her, does not respect me trying to quit :\

n  I quit smoking on Sunday evening. Day 3 today. I feel exhausted, annoyed, bored. But the fight must go on. Keep fighting :)

n  somebody is getting punched in the f***ing mouth today. #coldturkey




n  i’m cool, day 4 no cigs but my mom smokes, i stay with her, does not respect me trying to quit :\

n  I quit smoking on Sunday evening. Day 3 today. I feel exhausted, annoyed, bored. But the fight must go on. Keep fighting :)

n  somebody is getting punched in the f***ing mouth today. #coldturkey

METHODOLOGY & DATA COLLECTION

•  Identify smokers • Query Twitter firehose for cessation event tweets •  Sample 2000 users •  3 Mechanical Turkers per tweet for verification •  2 years worth of tweets per verified smoker (1 year before cessation event, 1 year after)

MEASURES Activity variables

•  Tweet volume, burstiness, frequency

Social variables

•  Friends, followers, tweets with @mentions, unique mentions

Personal & Emotional variables

•  Location, sentiment intensity

Behavior Change Process variables

•  Cessation date, motive to quit, treatment, stages of behavior change



Social variables








Social variables








Social variables








Social variables






RESPONSE VARIABLES  Outcome   Survival / Relapse

 Survivors  Congratulations to me, still smoke free J

 @username nope i don’t smoke anymore

 first few weeks were hard but I haven’t craved a cig in months

 Relapsers  Day 26: Broke down and bought a pack of smokes last weekend. Smoked the last one today.

 Well, tried to quit smokin tobacco but..had a fucked up day

 So day 3 of not smoking is about to get cut short..i can’t do it lol

ALIGNMENT WITH CDC REPORTS

! !

Men Women CDC 54% 46% Twitter 59% 41%

Location

Gender

Abstinence Rates

! !


! !


Location

Gender

Abstinence Rates

! !


! !


Location

Gender

Abstinence Rates

! !


! !


Location

Gender

Abstinence Rates

! !

RESULTS

•  Survivors (S) and Relapsers (R)

•  Before (B) and After (A) the cessation point

SIGNIFICANT DIFFERENCES: ACTIVITY

Tweets before

Tweets after

Burst before

Burst after

Freq before Freq after

FAIL 1243 3551 10.119 10.943 3.56 2.704

SUCCEED 412 771 4.459 4.278 9.906 11.254

TIME OF DAY

!“im really considering smoking tonight bcause im so stressed”

TIME OF DAY

!“outside the club and guy beside me smoking makes me wanna”

“im really considering smoking tonight bcause im so stressed”

SIGNIFICANT DIFFERENCES: SOCIAL

Friends before

Friends after Followers before

Follwers after

FAIL .093 .073 .074 .064

SUCCEED .187 .207 .114 .125

“Starting the patch today. Everyone please support me on the road to quitting smoking”

“Ok I started a really big challenge yesterday... I quit smoking! I may need some help from you guys in the upcoming days/weeks”.

SIGNIFICANT DIFFERENCES: SOCIAL

Friends before

Friends after Followers before

Follwers after

FAIL .093 .073 .074 .064

SUCCEED .187 .207 .114 .125

Day 2 of not smoking #bittersweet I quit smoking yesterday and everyone is pissing me off! Day 3 without a cig. Ooo I'm about to shoot someone

MOTIVES

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PREDICTION

CONTRIBUTIONS  Theoretical contributions   Goal setting   Behavior change

 Computational contributions   Classification of smoking-‐relevant content

  Extraction of informative data features   Modeling the process & predicting ultimate outcome   Design implications for intelligent intervention technologies

RESEARCH PROJECTS


Dimensions Mined:

Projects:




interface




SUMMARY & CONCLUSION

•  Advance our understanding of what our digital footprints reveal about us as humans

•  Develop new computational techniques that can make sense of and utilize this data’s nuanced semantic, psychological, and behavioral dimensions

•  Apply the resulting intelligent systems across multiple domains in order to help people use digital information and have meaningful experiences with technology

THANK YOU!

•  Advance our understanding of what our digital footprints reveal about us as humans

•  Develop new computational techniques that can make sense of and utilize this data’s nuanced semantic, psychological, and behavioral dimensions

•  Apply the resulting intelligent systems across multiple domains in order to help people use digital information and have meaningful experiences with technology

v  Questions, comments, and guidance welcome!

Elizabeth L. Murnane [email protected]

www.cs.cornell.edu/~elm236/