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Big Data

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by

Lili Schliesser

on 11 October 2013

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Transcript of Big Data

Scholarly Research
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Business & Politics
Advertising
Understanding what makes something memorable
Close the engagement gap between political candidates or companies and their audience
Identify opinion leaders
Data
What is Big Data?
*Big data is a new class of asset like currency or gold

*Big data acts as a predictor for events and trends

*Big data provides a picture of cultural norms, dialogue, trends and events that informs business, scholarship, legislation, art, eduction and so on
Big Data Tools
-Correlations
-Content
-Feedback
-Sentiment Analysis
-Social Media Monitoring
-Data Marketplaces
-API's
-Algorithms
Origins
Insurance Companies
Hedge Funds
Wall Street
-Math Modeling
-Predictive Algorithms
-Artificial Intelligence
Software
New Areas of Study
Changes to the User's Online Experience
*Searches based on the searcher not rankings provides more relevant and intuitive web searches

*Better ways to explore and enjoy stories and memories

*Visualization of information through Infographs

*Sentiment Analysis as a standard feature on search engines
Scholarly & Cultural
Applications
Big Data:
Quantitative Research in the Digital Age

Data+Processing=Insight
Words
Images
Video
Digitized Books
Websites
Blog Posts
Social Media
Communications
*tweet tweet*
Personal Opinions
The Industrial Internet

-equipment
-automobiles
-shipping crates
-meters
-and more
Digital sensors inside
Government Data
www.data.gov
Search Engine Information
*Google
*Bing
Weather
Demographics
Maps
Cell Phone Data
*Reviews
*Ratings
*Reccomendations
Data +
INSIGHTS
*Scholarship
*Social Change
*Business
*Politics
*User Experience
*Public Health
*International Development
Cliometriecs
Stylometry
Sociology
Social Psychology
Political Methodology
Linguistics
Human Geography
Communication Studies
Philosophy
Twitterology: The Study of Behavior and Language Via 140 Characters
Culturomics: study of the wide array of new digital phenomena spanning the social sciences and the humanities
Social Genome Mapping: mapping of users' electronic makeup and connections
Crowdsourcing: the practice of obtaining needed services, ideas, or content by soliciting contributions from online communities
Extract trends
Google searches predict home sales better than real estate economists
Tailored Products and Services
Product planning
Real feedback-no need to speculate
Focus Groups without size limits
Cater to customer's tastes
Customer Care
Knowing where customers came from (websites), how they viewed your page (iPhone, computer) Customer opinions are readily available through feedback forms and discussion forums
Strategy
Forecasting the impact of a newspaper's editorials on a company's stock price
Pinpoint effects of specific issues on customer perceptions to adjust marketing and public relations techniques
Small businesses can compete with those with greater resources
Predict which customers will visit again, what products interest them and what special offers will appeal to them
Ranking businesses for things like greenest, women friendly, etc.
Criticisms of Big Data
*Who is generating the information and how do they represent the total population?
*Privacy issues with conceptual, legal, and technological implications
*Words do not equal moods because of the subjective nature, subtleties, irony, slang, sarcasm, etc.
*Math models are like metaphors in that they over-simplify
*Will Big Data usher in a digital surveillance state mainly serving corporate interests? aka Is Big Data really Big Brother?
*Algorithms are too simple to apply to human behavior
*Huge data sets and fine-grained measurements increase the number of false discoveries
*Unfair or discriminatory statistical inferences may effect products, bank loans and health insurance
*When humans are overloaded with choices they tend to copy others and follow trends. Correlations do not equal causation.
*Withholding data due to competition or secrecy effect data outcomes
NEEDS:
*Workers with a knowledge of a subject and an understanding of natural, intuitive language.
*Understanding the demographics behind content. Who uses SNS & Blogs?
*Pondering the ethical dimensions of this work rather that just the math
*Sophisticated Sentiment Analysis algorithms & human annotators to verify results
*Clean and correct data that accounts for local variations
*Privacy safeguards for corporate and personal information
*Moving older data online
Big Data Tools
Data Repositories & Marketplaces
Correlations
Content
Feedback
Data Pulls
Infographics
Sentiment Analysis
Google Books + Magazines
Videosense
Videosurf
Digitalsmiths
N-Gram
Talk Meters
Facebook apps
eCommerce software

Magento Eterprises

Message Boards
API's
Datasift
Gnip
Infochimp
Algorithms Distinguishing context of words
Sentiment Metrics
Tweetfeel
Twendz
Twitrratr
SentiRate
CommonCrawl
Windows Azure Marketplace
Google Ranking
Google Correlate
Google Analytics
Bing Webmaster Tool
Taykey
Chartbeat
Radian6
Visual.ly
Data + Processing
Perform rigorous quantitative inquiries into social sciences & humanities
Trace word patterns & thematic elements in literature and media
Understanding collective memory through various perspectives
Fitness or evolutionary strength of language and ideas
Define and predict movement and social change through associated media
Looking for agenda phrases and depictions in media
Locating origins and historic parallels
Understanding the demographics of a movement
Understanding affective response
Full transcript