Social Foraging
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Social Foraging
Dynamics of Social Interaction
Curated by Ashish Umre
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Rescooped by Ashish Umre from Influence et contagion!

Detecting Automation of Twitter Accounts Are You a Human, #Bot, or #Cyborg? | #contagion

Via luiy
luiy's curator insight, June 12, 2014 2:23 PM

We first conduct a set of large-scale measurements with a collection of over 500,000 accounts. We observe the difference among human, bot, and cyborg in terms of tweeting behavior, tweet content, and account properties. Based on the measurement results, we propose a classification system that includes the following four parts:


1) an entropy-based component,


2) a spam detection component,

3) an account properties component, and

4) a decision maker. It uses the combination of features extracted from an unknown user to determine the likelihood of being a human, bot, or cyborg.

Our experimental evaluation demonstrates the efficacy of the proposed classification system

Rescooped by Ashish Umre from Influence et contagion!

Twitter #bots in class. You're here because of a robot | #datascience #agents #influence

Twitter #bots in class. You're here because of a robot | #datascience #agents #influence | Social Foraging |
Note: This post is co-written with Piotr Sapieżyński Is it possible for a small computer science course to exert measurable influence (trending topics) on Twitter, a massive social network with hun...

Via luiy
luiy's curator insight, March 21, 2014 12:34 PM

A large part of our motivation for investigating Twitter bots in class is that the amount of manipulation that humans are experiencing on line is ever increasing. Think, for example, about how Facebook’s time-line filtering algorithm shapes the world view of hundreds of millions around the globe. And that’s just the most main stream example.


Social influence


As the course progressed, we focused on creating bots that could use machine learning to recognize “good” content for tweeting and retweeting. Bots that are able to detect topics within their tweet-stream … and distinguish between real, human accounts and robots among their followers.

However, the question remained: Can those thousands of followers  be converted to influence on Twitter? For the class’ final project, we decided to put that to the test.

The overall goal was to for each team to build a convincing bot, get human followers, and  at a specified time, for everyone work together to make specific hashtags trend on twitter. So how to achieve that goal? Here’s an overview of what each team has worked on:


- Build convincing avatars and use the high follower-counts as part of the disguise. 


- Use machine learning to tell who’s a bot and who’s not (in order to focus only on humans and ignoring bots). 


- Use natural language processing & machine learning to discover quality content to re-tweet and tweet. 


- Use network theory, to explore the network surrounding existing followers, making sure that bot actions reach entire communities.