Analysis of online hate communities in Social Networks

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Analysis of online hate communities in Social
Networks

• Presented by
• Ruchi Bhindwale

2
OUTLINE

• Introduction
• Related Work
• Analysis
• Our Approach
• Data Preprocessing
• Graph Creation
• Manual Mining Results
• Advantages/Disadvantages
• Conclusion

3
Introduction

• Web 2.0
• Blogsphere Social
  Networking
• 
  Sites
• Hate Groups

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Related Work

• Often Social Networks as represented as a graph
• Approaches to identify communities
• Co-citation Analysis
• Hidden Markov Model
• Content Analysis

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Analysis

• One supporter and many opponents
• 98 were in the category Countries and Regional
  and Religion and Belief
• All the communities with hate title do not have
  posts with hate content
• Such communities contained foreign language words

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Our Approach

• Combination of content (text) mining and graph
  mining.
• Text mining is employed to deal with the posts
  while graph mining considers the communication
  pattern within these communities.

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Data Preprocessing
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Rules for generating nodes and edges

• Each Member as a node.
• A directed edge between nodes for the message
  posted by one member, addressed to the other
  member in a particular discussion thread.
• Self loop edge for the member who creates a new
  hate thread.
• The message not addressed to anybody is
  considered as addressed to the creator of the
  thread.

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Weighing scheme

• Weights are assigned to edges according to degree
  of hate content of the corresponding messages.
• Positive weight for the message that support the
  topic of the community and negative for opposing.
• Different weight values are assigned. E.g. 1 for
  normal, 2 for high and 3 for very high hate or
  anti-hate content.

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Graph Characteristics

• Reveals two communities inside one community. One
  who supports the community and the other who
  opposes.
• Very less communication inside these sub
  communities.
• Easy to identify the members who spread hate
  heavily by the weight of the edges going out from
  the node corresponding to that member.

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Manual Mining Results

• ASU MS 2006
• Microsoft Corporation
• Cricket Fans
• Linux Kernel Programmers
• We hate India
• USA Democrats
• Communism
• Hate Israel
• Data Mining and KDD
• We hate exams
• Hate Pakistan
• Brad Pitt Fan club
• For those who hate idol worship
• Hate Indian Muslims
• Buddhism

• 25 communities were selected
• Resulting Set obtained was manually validated

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Step 1(Select Category)
We hate India Hate Israel We hate exams Communism USA Democrats Hate Pakistan For those who hate Idol worship Hate Indian Muslims Buddhism ASU MS 2006 Microsoft Corporation Cricket Fans Linux Kernel Programmers Data Mining and KDD Brad Pitt Fan club
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Step 2 Step 3
We hate India Hate Israel Hate Pakistan For those who hate Idol worship Hate Indian Muslims Communism USA Democrats Buddhism
We hate India Hate Pakistan For those who hate Idol worship Hate Indian Muslims Hate Israel
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Step 4(Number of threads)
We hate India Hate Pakistan Hate Indian Muslims For those who hate Idol worship
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The Graph
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Advantages and Disadvantages of the approach

• The Approach clearly reveal basic communication
  pattern in a hate community.
• Can easily identify the hate spreading people.
• Difficult to measure degree of hate content as
  hate content tend to be very subjective.
• Not easy to figure out that - To whom a
  particular message is addressed in an ongoing
  discussion, when it is not explicitly cited.

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Conclusion

• Hate community targeted to a country or a
  religion usually contains high amount of
  offensive content.
• For social networking websites providing features
  to create communities and discussion boards
  inside such communities, detecting hate
  communities has become very important.
• We have tried to give a model to analyze such
  offensive hate communities.

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• Thanks to Nitin and Lei