dh 199 social media analytics

DH Capstone Social Media AnalyticsMarissa Clifford, Stephanie Wong, Swati Katta, Jonathan Solichin, Joanna Chen, Thanks to Professor Todd Presner, Professor Francis Steen, Yoh Kawano, and David Shepard

The Big Question Then

● Develop World Watcher: A Tool for Automatically Discovering and Recording Events as they are Represented in Social Media ○ Index Users○ Compare elite and social media

Adapting Our Methods and Goals

● Find reliable and sustainable method for identifying and monitoring Index Users

● Gathering data and trying new approaches● Trend toward visualization

The Big Question Now

● Agenda Setting● Primacy in the News● Analogical Reasoning● Explicit Causal Reasoning● Duration and Persistence● Sentiment Analysis

Background: Boston Bombings Research

1) Evidence - What Happened?

2) Explanation - How did it happen?

3) Event Surgery - Why did it happen?

4) Accountability - Who is to blame?

5) Planning - How to prevent it?

Towards a Sentimental Dictionary: Emotional Intensity

0 1 2 3

Highly Subjective Editorial

Completely Neutral Reportage

Just The Facts

• All CAPS• Expletives• !!-!!!

Data

● Twitter● Reddit

Twitter Data

● Twitter’s API was very useful in obtaining the data from the

live stream

● Initially, we decided to find some index users who we could

watch for tweets related to important events

● These index users were selected on the basis of several

parameters

Index Users

● Sample the live stream for tweets and get the twitter handles

of their authors

● Determine if the author has the following:

– High Klout score

● Score obtained from Klout API

– High number of followers

– Geographic location : United States

Twitter Timelines

We obtain the timelines of each of those index users and analyze

whether they are talking about important and interesting

events.

Main issue : SPAM!!!

Reddit Data

● Another source of news and information about

events

● More anonymous unlike twitter which is focused

on the user

● Could help in filtering tweets from twitter

● More organized

Using Reddit to filter Twitter

● Fetch Reddit headlines

–Reddit API is not useful

● Use selected keywords to filter live stream of

twitter

Learning Curve

● used D3 examples● replaced with our dataset● altered the json and html codes● created our own tutorial

Visualizing the Data

Picking a visualization

Using Anvil

ONTOLOGIES

Visualizing the Ontology

1. Parse all tweets and get word frequency count

2. Put frequency count into the ontology hierarchy

3. Visualize

(Each outer radius represents subdivision of the inner radius, starting with all tweets)


eg.We can see that misogyny makes a lot on the topic of women, #yesallwomen makes up a lot on the topic of misogyny, and violences makes a lot on the topic of #yesallwomen.


eg.We can see that killing makes a lot on the topic of shooting, rampage makes up a lot on the topic of misogyny.

and so forth...

Demo

http://sandbox.idre.ucla.edu/up206b/2014/dh199/jssolichin/sunburst3/

Issue with this viz

● Tweets are combined into one document, and thus the graph does not represent # of tweets, but the number of mentions ofthe topic.

Visualizing the Ontology Over Time

1. Group tweets by hour. (x axis)2. Word frequency count each group (y axis)3. Visualize


Mentions on shooting makes up the bulk of Tweet.


We can see topic enter and leave.eg. Rampage starts early but tapers off.


Sex enters somewhere in the middle and surges briefly before diminishing.


Sorority comes close to the end of the data briefly, but strongly

Demo

● http://sandbox.idre.ucla.edu/up206b/2014/dh199/jssolichin/steamgraph/


● Conversation changes over time● Start from fundamental facts to exploring

cause/social constructs?

Future:● Overlay elite media publishing to see how

they affect conversation.● Overlay time to see why there is period of up

and down.

Moving Forward

● Characterize and visualize the recursive relationship between media

● Build a Foundation○ Event Modeling○ Use Existing Data

Future Goals To explore and answer the questions and relationships between new cycles on social media vs broadcast media through data collection and information visualization. How do news stories develop differently in social media vs broadcast media?-in terms of timeline, what topics are covered, and references that are made

FUTURE GOALS

Final Presentation saved at:http://prezi.com/kluuirlfzfol/dh-capstone-social-media-analytics/

http://prezi.com/kluuirlfzfol/dh-capstone-social-media-analytics/



dh 199 social media analytics

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