Analysis of online hate communities in Social Networks Presented by : Ruchi Bhindwale

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

Introduction Web 2.0 Blogsphere Social Networking Sites Hate Groups

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

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

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.

Data Preprocessing Select communities related to country and politics Mine the title with “hate keyword” Consider only those communities with substantial number of members Mine the thread title to select relevant posts Consider only those posts with substantial number of replies Text mine the post to provide a hate content Representation the communication as a graph

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.

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.

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.

Manual Mining Results 25 communities were selected Resulting Set obtained was manually validated 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

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

Step 2Step 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

Step 4(Number of threads) We hate India Hate Pakistan Hate Indian Muslims For those who hate Idol worship

The Graph

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.

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.

Thanks to Nitin and Lei