Jasper snyder social media research - 2012

A Presenta*on from the NewMR “Social Media Research” Event

9 October, 2012

All copyright owned by The Future Place and the presenters of the material For more informa:on about NewMR events visit newmr.org

Enriching Social Data for Market Research Jasper Snyder, Converseon

© 2011 Converseon Inc. Proprietary and Confidential Speaker: Jasper Snyder, Converseon

NewMR Social Media Research Event, 9 October 2012, Session 2

Enriching Social Data for Market Research

#NewMR

Jasper Snyder 9 October 2012


NewMR Social Media Research Event, 9 October 2012, Session 2 3

Questions for Today’s Session

•  What sorts of questions can

social media research and

analysis answer?

•  How do you move from millions

and billions of un- and semi-

structured documents to

actionable market research

insights?

•  How can social media-based

market research be tailored to

different client use cases?

•  How does machine learning

work, and why does it matter for

market research?



Social-media research can support both traditional market research goals and PR functions.

Communications Functions through Social Media Monitoring

Traditional Market Research through Social Media Listening

•  Thoughts and opinions about products and brands

•  Market awareness of products or brands

•  Purchase triggers

•  Consumer complaints and product malfunctions

•  Crisis monitoring and

response

•  Reputation management



Social Media Monitoring

5

These two use cases – market research and communications – closely align with two services.

Social Listening

When what matters most is understanding a consumer segment or market.

Goal is to understand a population “out there” in the world. Higher tolerance for missing content. Lower tolerance for irrelevant content.

When what matters most is delivering customer service, navigating a crisis situation or detecting reputation threats. Goal is comprehensive, real time coverage. Higher tolerance for irrelevant content. Lower tolerance for missing content.



The Social Media Research Process: From Raw Data to Insights

2. Data Enrichment

3. Analysis & Insight

Generation

1. Data Collection



Stage 1: Social Data Collection

2. Data Enrichment


Generation

1. Data Collection

Primary Challenges:

1.  Pull in relevant data and metadata

2.  Coverage of appropriate social media channels

3.  Eliminate spam and irrelevant content.

Primary Goal:

Identify and acquire the data that can answer your business questions.



Stage 2: Data Enrichment

2. Data Enrichment


Generation

1. Data Collection

Primary Challenges:

1.  Data normalization

2.  Classification

3.  Scalability

Primary Goal:

Implement document- and sub-document-level enrichments like topic, consumer segment, emotion and sentiment.



Stage 3: Analysis & Insight Generation

2. Data Enrichment


Generation

1. Data Collection

Primary Challenges:

1.  Reliability

2.  Strategic Value

Primary Goal:

Connect the dots between a suite of metrics and data points in order to reach sound strategic conclusions.



Social Media Channel Approx. Monthly Volume Furthermore…

Blogs 30 million new posts On-site comments and social cues and sharing

Facebook 1.8 billion status updates Social cues (e.g., “likes”) and comments

Twitter 4 billion tweets Social cues like favoriting and flagging other users

YouTube 400 million social actions 240 years of video content uploaded each month

10

Social media represent a massive compendium of documents…



…and these documents are all of different types.



Harvesting Data and Metadata from Social Media Documents: A Tweet Dissected




•  Author Name •  Text •  Publication Date •  Some hashtags

Datapoints:




•  Person or tweet that a tweet is in reply to

•  Follower count of author •  Times retweeted •  Times favorited •  Author description

Metadata:



Sorting Social Metadata

Tweets that contain #Ford in the text.

A

B

C



Relevancy as a Sorting Task…

Relevant Documents

Irrelevant Documents

All Documents Containing Your Boolean Query

All Social Media Documents •  Spam

•  Documents not in target language (e.g., not English)

•  Contain keyword but not relevant to client question



Data Enrichment: What Should We Measure?

Metric Explanation Sentiment Does the author make a negative or positive

point about a product or brand? Topics What topic is the author talking about the

product or brand in relation to? Purchase Stage Has the author of a document already

purchased the product when writing about it online?

Consumer Segmentation What segment is the document’s author from?

Emotions What emotions do authors express toward the target brand or product?



Data Enrichment: What Should We Measure?

Metric Sorting Categories Sentiment Positive, negative, neutral Topics Pre-selected topic and unexpected topics Purchase Stage Before making a purchase or after. Consumer Segmentation Young male, middle-aged woman, etc. Emotions Joy, anticipation, surprise, fear, etc.



How can we implement the sorting tasks we’ve discussed so far?

Sorting Tasks

Human Sorters Machine Sorters



Q: How do you know when a computer is correct?

A: The same way you know that a human is correct:

“I know it when I see it…”



Establishing A Basis for How Well Humans Agree With One Another

Tweet Coder 1 Coder 2

I do not like the cats with thumbs “advert”

Disgust Anger

I say that video is real, definitely.

Trust No Emotion Expressed

Item Coder 1 Coder 2

1 Positive Positive

2 Positive Neutral

3 Neutral Neutral

4 Negative Positive

etc. … …

Example 1: Inter-Coder Agreement on Sentiment Example 2: Inter-Coder Agreement on Emotion



Using Human Parallel Coding to Establish Gold Standards

Confusion Matrix: Human as Gold Standard

Raw Accuracy: 61.5%

POSITIVE NEGATIVE NEUTRAL TOTAL POSITIVE 365 24 159 548

NEGATIVE 57 81 65 203 NEUTRAL 274 60 415 749

TOTAL 696 165 639 1500



Using A Credit Matrix to Create Improved Measurement

POSITIVE NEGATIVE NEUTRAL POSITIVE 100% 0% 50%

NEGATIVE 0% 100% 50% NEUTRAL 50% 50% 100%

Credit Matrix

POSITIVE NEGATIVE NEUTRAL POSITIVE 365 24 159

NEGATIVE 57 81 65 NEUTRAL 274 60 415

Confusion Matrix: Human 1 as Gold Standard

Partial Credit Figure of Merit: 82.3%



But how does the machine learn?

1.  Collection of Human Annotated Data

2.  Machine ingests coded data and finds patterns in each category classification

3.  Machine applies model from step two on raw data. Results are compared to human coding of same material.



In conclusion….

•  Get specific about your use case

– is it market research or

monitoring?

•  Select metrics that speak to your

business questions

•  Take advantage of data (e.g., the

text of what people say), but also

meta-data (e.g., the additional

information that most social

media documents contain).

•  Understand that machines will

rarely make “perfect” decisions,

because classifications and

judgments are inherently

subjective, but we can help them.



Ray Poynter VCU, Vision Critical

Q & A

Jasper Snyder Converseon



Jasper Snyder, VP, Converseon [email protected]

Thank you

A Presenta*on from the NewMR “Social Media Research” Event

9 October, 2012

All copyright owned by The Future Place and the presenters of the material For more informa:on about NewMR events visit newmr.org

Enriching Social Data for Market Research Jasper Snyder, Converseon

Marketing

Jasper snyder social media research - 2012