Twitter-Bot-Detection-With-Graph-Analytics

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Twitter-Bot-Detection-With-Graph-Analytics

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Twitter-Bot-Detection-With-Graph-Analytics 최신버젼 다운로드

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#  Detection Of Fake Twitter Bots Through Unsupervised Learning
CSE 573 - Semantic Web Mining - Spring 2020 - Group 4 - Project 6 
#

## Team Members:
* Aditya Narayanan (1215232278)
* Vasishta Harekal (1215278298)
* Sumukh Ashwin Kamath (1217723013)
* Renu Kadolkar (1215200727)
* Hang Zhao (1214868408)
* Cheng Chen (1216700771)
* Shankar Krishnamoorthy (1217214947)

## System Architecture
![Image description](https://github.com/vasishtahd/Twitter-Bot-Detection-With-Graph-Analytics/blob/master/system_design.png)

## Installation Instructions:
* Using Google Collab.
  * Navigate to ```code/final_node2vec_shingle_implementation.ipynb```
  * Request Permission from one of the group meme
  * Click on the "Open in Collab" Link in the python Notebook.
* Local Machine.
  * Run ```pip install requirements.txt``` to add all dependencies into you system.
  * Update the Dataset links in the ```code/final_node2vec_shingle_implementation.ipynb```
  * Run ```code/final_node2vec_shingle_implementation.ipynb``` onto your local and get the output
##  Node2Vec
*   In the Node2Vec Implementation, we have used the following parameters. <br>
    ```node2vec = Node2Vec(G, dimensions=40, walk_length=8, num_walks=30)```

##  Shingle
*  We have used the following parameters for Min-Hashing and Locality Sensitive Hashing <br>
   ```minhash = MinHash(tweet_content, n_gram=9, permutations=100, hash_bits=64, seed=3)``` <br>
   ```lsh = LSH(minhash, labels, no_of_bands=50)```

## References
* Nguyen Vo, Kyumin Lee, Cheng Cao, Thanh Tran, and Hongkyu Choi. 2017. “Revealing and Detecting Malicious Retweeter Groups”. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 (ASONAM ’17). Association for Computing Machinery, New York, NY, USA, 363–368. DOI:https://doi.org/10.1145/3110025.3110068

* Chavoshi N., Hamooni H., Mueen A. (2016) “Identifying Correlated Bots in Twitter”. In: Spiro E., Ahn YY. (eds) Social Informatics. SocInfo 2016. Lecture Notes in Computer Science, vol 10047. Springer, Cham

* Jiang, Meng & Cui, Peng & Beutel, Alex & Faloutsos, Christos & Yang, Shiqiang. (2016). Catching Synchronized Behaviors in Large Networks: A Graph Mining Approach. ACM Transactions on Knowledge Discovery from Data. 10. 1-27. 10.1145/2746403.

* Mohammadrezaei, Mohammadreza & Shiri, Mohammad & Rahmani, Amir. (2018). Identifying Fake Accounts on Social Networks Based on Graph Analysis and Classification Algorithms. Security and Communication Networks. 2018. 1-8. 10.1155/2018/5923156.

* J. Jia, B. Wang and N. Z. Gong, "Random Walk Based Fake Account Detection in Online Social Networks," 2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), Denver, CO, 2017, pp. 273-284.

* Node2vec, Available: https://en.wikipedia.org/wiki/Node2vec

* Fabio Giglietto, Nicola Righetti, Luca Rossi, CoorNet, Available: https://github.com/fabiogiglietto/CooRnet Last Accessed: 04/26/2020

*  https://snap.stanford.edu/node2vec

*  https://github.com/eliorc/node2vec

*  https://towardsdatascience.com/node2vec-embeddings-for-graph-data-32a866340fef

*  https://cs.stanford.edu/~jure/pubs/node2vec-kdd16.pdf

*  https://pypi.org/project/snapy/

*  https://medium.com/@jonathankoren/near-duplicate-detection-b6694e807f7a

*  https://minteressa.github.io/modelling/2016/06/20/near-duplicate-detection.html

*  https://pradeepprabakar.wordpress.com/2012/05/30/near-duplicate-detection-in-streaming-twitter-data/

*  http://www.softcorporation.com/products/neardup/

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