Information integrity from source via retweets
Lamplight Research Direction Proposal
Agenda
- Problem Statement
- Hypothesis
- Flow
- Information Cascade Tree
- Concept of Speed, Scale, Range
- Questions
- Next Steps
Problem Statement
How does the integrity of
entertainment, political & technology
topical information on Twitter evolve via
retweets and comments?
Hypothesis
*Dependent on the data
*Depends on the experimental design
Information diverge as they travel through the networks
Preservers
- maintain the original intent, context, and content
- a continuum between preserving content and meaning of a tweet
- e.g. @DanFan: I shorten the words, del unnecessary [puctuation,] ... but don't change meaning or attribution
Adapters
- willing to remove various parts of the tweet to suit their own purposes
- e.g. 1. remove some or all of the original tweeter's comment, leaving just the URL
- e.g. 2. Write their own text or paraphrase the original tweet
- e.g. 3. truncates original msg to make it fit ( - context)
Reasons for Info Loss/Gain
Boyd D. et al. "Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter" (2010, IEEE)
Twitter etiquette
For all retweets, follow one the following formats
1. RT @user URL
2. retweet @
3. via @
Implications of Info Loss/Gain
1. Change in intellectual ownership of the substantive content of the message
2. Meanings of the original message changes as they spread across the network
Flow
Current Topical Data from Lamplight
(Star Wars, Uber, Paris Attack, US Election)
Identify hops
Investigate integrity of diffused info from Seed
Seed
A
B
C
N
Social Influence
Way of Transmission
Topic
Global influence: celebrity, epidemic
Local influence: immediate circle of followers
Retweet (RT)
RT + Comment (RT + C)
Entertainment
Politics
News
Technology
Method
2 * 2 * 4 matrix comparative study
User graph: scale and range of a seed
Evaluate integrity of tweet at node (n) against seed or source
Info Cascade Tree
Speed
Range
Scale
Integrity of Info
RT
RT
RT + C
RT + C
RT + C
RT + C
RT + C
Questions
Does Lamplight's current nlp algorithm cover the ability to differentiate the tiers of users?
-- construction of the user graphs (seed and nodes)
Does lamplight's current algo include a way to verify and present a confidence level of the integrity of the information?
-- assessment of the integrity of information between nodes and between node (n) and seed
Next Steps
With lamplight topical data, start to form
1. User tree
2. Hypothesis around information integrity and social influencers (Global and Local)
3. Ways to measure integrity maintained and sentiment change
Lamplight Information Integrity via retweets
By Y Yang
Lamplight Information Integrity via retweets
How does social influencers affect the integrity of entertainment, political, and technology information from the source?
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