University of Michigan · Raccoon: A Query System for Social Media Signals Architecture Dolan Antenucci, Michael R. Anderson, Michael Cafarella University of Michigan Real world phenomena

Online Operation

Aggregation

Web UI

Relevant Topic 1Relevant Topic 2...



Scoring and

Ranking

Offline ProcessingSocial Media

Topic-Signal DB

Topic-Signal Creator

job, unemployed, hiring

2012 2014

User Query

Topic-Signal DB

Raccoon: A Query System for Social Media Signals

Architecture

Dolan Antenucci, Michael R. Anderson, Michael CafarellaUniversity of Michigan

Real world phenomena like unemployment and disease behavior have been estimated using social media – a process known as nowcasting [1].

These estimates are cheaper and faster than traditional data collection methods like phone surveys.

Faster and cheaper means saved money and better policy.

Motivation: Forecasting the Present via Social Media Example User Query (Target: Unemployment Behavior)

Problem: Nowcasting Requires Ground Truth Data

Scalability with the CloudCurrent Prototype System:•  40 billion tweets (collected 2011-2015)•  150 million topic-signal pairs (after threshold filtering)•  Query processing runtime: ~20s (on 10 EC2 c3.8xlarge servers)•  Topic-signal pairs support daily updating

[email protected] w http://web.eecs.umich.edu/~dol

[1] S. L. Scott and H. Varian. Bayesian variable selection for nowcasting economic time series. In Economics of Digitization. University of Chicago Press, 2014.[2] A. Greenspan. The Age of Turbulence. Penguin Press, 2007.[3] D. Antenucci, M. Cafarella, M. C. Levenstein, C. Re ́, and M. D. Shapiro. Using social media to measure labor market flows. Working Paper 20010, National Bureau of Economic Research, March 2014.

Related Work

10/17 i need a job 491

12/15 i love you 5092

1/28 jus8n bieber 940,291

2.

3.

“Ground Truth Data”

1.

Apr May Jun

Une

mploymen

t

Apr May Jun

Une

mploymen

t

“I need a job”, ) ( “I need a job”, ) ( “I need a job”, ) (

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job, unemployment

2012 20142013 2015

Users Given Trade-Off Between Query Components

2012 20142013Correlation with query signal: 0.897

2015

Topics Used: • job new year • job interview tomorrow • first job interview today • working two jobs • job interview • #hiring #jobs • ...

Resulting Target Estimate:1

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Topic-Signal Pairs Nowcasting Result

Like a search engine, users iteratively submit queries:

Raccoon User

Raccoon Nowcasting

System

User Query (q, r)


2015



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Nowcasting Result (T, s, β)

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job, unemployment

2012 20142013 2015

User Interaction Loop

Unemployment-‐related keywords

Seasonal trends of unemployment (drawn by user)

Yet, economists [3] still want estimates for targets lacking ground truth data:

Solution: Substitute Domain Knowledge for Ground Truth

Our target users have some semantic and signal domain knowledge about their target phenomena:

Seman=c Knowledge (keywords, etc.)

Signal Knowledge (seasonal trends, etc.)

Apr May Jun

Une

mploymen

t

Nowcasting Result

“I need a job”, ) ( “I need a job”, ) ( “I need a job”, ) (

User’s Domain Knowledge

“Ground Truth Data”

•  10-day auto sales (once used, but no longer available [2])•  Physical movement (e.g., out of parents house, for job)•  etc.

Topic-Signal Pairs

Prefer Signal Query Component

Users can explore a set of query results in real-time, produced by varying the relative influence of the semantic and signal query components:

Prefer SemanticQuery Component

User Query

Signal Scorer

Seman=c Scorer

Trade-‐off Generator

Topic 1 Topic 2 … Topic 1 Topic 2 … Topic 1 Topic 2 …

Feature Reduc=on via PCA

Topic 1 Topic 2 … Topic 1 Topic 2 … Topic 1 Topic 2 …

Topic-‐Signal DB

Final Aggrega=on via Linear Regression


2015



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2015



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Scoring and Ranking Aggregation

Documents

University of Michigan · Raccoon: A Query System for Social Media Signals Architecture Dolan Antenucci, Michael R. Anderson, Michael Cafarella University of Michigan Real world phenomena