Difference between revisions of "Analysis of Twitter user's location behaviors"
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In order to make their location interpretable by us, we enforce them to be in Great Pittsburgh area. | In order to make their location interpretable by us, we enforce them to be in Great Pittsburgh area. | ||
* Extended user set (EUS) : we extract all the mutual followers of users in SUS in order to observe the social sphere of the seed users. | * Extended user set (EUS) : we extract all the mutual followers of users in SUS in order to observe the social sphere of the seed users. | ||
| − | (if user A is in SUS, A follows B and B follows A, then B is in the EUS. B does not have to be using location feature) | + | (All SUS users are in EUS. if user A is in SUS, A follows B and B follows A, then B is in the EUS. B does not have to be using location feature) |
Then we crawl user profiles and tweeting history for all the users in EUS in a specified time period (say 60 days) using REST API, in order to study the nature of their tweeting behavior using location. | Then we crawl user profiles and tweeting history for all the users in EUS in a specified time period (say 60 days) using REST API, in order to study the nature of their tweeting behavior using location. | ||
Revision as of 23:19, 8 October 2012
Contents
What's the team
What’s the data you’ll work with?
We first select a list of users in twitter who utilize location information in their tweets.
- Seed user set (SUS): we randomly gather a set of users (say 500) that are "location-active" (It means they use location information in their tweets.).
We identify them by listening to streaming API and apply filtering rules for SUS. In order to make their location interpretable by us, we enforce them to be in Great Pittsburgh area.
- Extended user set (EUS) : we extract all the mutual followers of users in SUS in order to observe the social sphere of the seed users.
(All SUS users are in EUS. if user A is in SUS, A follows B and B follows A, then B is in the EUS. B does not have to be using location feature)
Then we crawl user profiles and tweeting history for all the users in EUS in a specified time period (say 60 days) using REST API, in order to study the nature of their tweeting behavior using location.
What’s the task or tasks?
Research questions:
- RQ1: What is the fraction of users that use location feature in Twitter? And how frequent are they using it?
- RQ2: What are relations between location and tweet content among "location-active" users?
- RQ3: Does "location-active" users share common posting behavior with their social sphere?
- RQ4: How is the impact of location info in the "location-active" users' social sphere?
- RQ5: What can we infer from users' location info? (Can we locate their living area, their home or their workplace? )
How will you evaluate? (qualitative or quantitatively?)
- To evaluate our analysis on tweets, we can pick a random subset of users, and compare our inferred user info with human-interpreted information.
- And to evaluate the consistency of our conclusions, we may use cross-validation.
(We use a subset to learn the nature of tweeting behavior, and check if the conclusions can also be applied to the rest of users.)
What are the key technical challenges, and what do you hope to learn?
- One of the challenges here is to infer users' behavior from tweet stream, which is a noisy channel.
Users might not have consistent tweeting style, or behave differently in different sources (for example, web browser V.S. mobile clients)
- Another challenge is the analysis of social interaction of users. The interaction flow that we can observe might be incomplete.
What kinds of social impact can we identify? We might need to dig deeper into our data to find the answer.
