Introduction

In this post I’ll be setting the stage for the next steps of the project.

Data

OK, let’s have a look at the main things we can get from Twitter:

1. Tweets (content)
2. user information
   1. her “opinion” (vote?)
   2. who gets re-tweeted a lot (influence?)
3. Conversations

To harvest this, we’ll -at least- need to:

1. “Read” the tweets
2. Process them linguistically to determine their sentiment and mentions of @party or @partymember
3. Annotate and store the tweets for later processing

Main challenge

A big question is as [Daniel Gayo-Avello] states correctly (See Flaw number 3) is: what constitutes a vote?

For instance, does the fact that user @voter speaks positively of @party mean that she will vote for @party?

Or, another example: do, say, 10 positive tweets about @party equal one vote for @party?

Or, the other way around: does one negative tweet about @party equal one less vote for @party?

Other Challenges

• What constitutes the opinion of a user?  Can we even determine it properly?
• How to filter the babble, the bots, the spinners? Is this even possible?
• How do we tackle the skewed demographics issue?

Next steps

In the next blog post we’ll start by defining what constitutes a vote in our case.  Don’t expect a perfect, not even a correct definition.

Again, I mainly want to look at what we need to collect and query big data.