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The News Threads project analyzes news articles to help find similarities between news articles and trace news provenance across time.

• Python 3.6 or newer
• Data in the correct input format.
• Change the input data path in news_threads.conf.
• The news_threads.conf file contains information about the config format.

• Input data is a single CSV named documents.csv with news articles in the folder specified in news_threads.conf
• Format is: (canonical_url, title, doc_id, domain, text, first_scrape_date)
• You may change the format by modifying the "build_minhash" method in dbscan.py
• Additional fields are ignored by default
• Field description:
  • canonical_url: URL of the news article
  • title: Title of the news article
  • doc_id: A unique ID for the article. This may be text or numeric.
  • domain: Domain on which the article was published. "apnews.com", "cnn.com", etc.
  • text: Article text
  • first_scrape_date: Date the web crawler first downloaded the article.

1. run "make init" from the root of the folder to build a Python virtual environment with all dependencies
2. Type "source .env/bin/activate" to launch the virtual environment.
3. Type "python -m newsthreads" to run the news threads pipeline

Note: These instructions were verified using Python 3.8 installed from the Microsoft Store

1. "python -m venv .env"
2. ".env\Scripts\activate.bat"
3. "pip3 install -r requirements.txt"
4. Type "python -m newsthreads" to run the news threads pipeline

• document_metadata.csv: "documentId, firstScrapedDate, title" for every document. This puts the document into a standard format so other pieces of code can read it.
• hierarchy_graph_edges.csv: This file contains two row formats intermingled. The purpose of this file is to determine the hierarchy of clusters by epsilon value (which clusters are parents of other clusters).
  • Leaf-rows: These rows have documents that no longer fall in a cluster at a specific epsilon value. The format is: "previousClusterId_previousEpsilon, documentId, 1"
  • Non-leaf rows: Clusters in these rows are not leafs in the hierarchy. The format is: "previousClusterId_previousEpsilon, clusterId_epsilon, count"
    • previousClusterId: ID of the cluster that is the parent of the current cluster in the hierarchy.
    • previousEpsilon: Epsilon at which the previous cluster was generated.
    • clusterId: ID of the current cluster. This cluster is a subset of the parent cluster.
    • epsilon: Epsilon at which the current cluster was generated. This will be previousEpsilon - 1.
    • count: Weight of the link between previousClusterId and clusterId at their respective epsilons. Higher weight means more documents are in the child cluster.
• Clusters (DBSCAN)
  • epsilon-cluster.csv: "epsilon, clusterCount" Number of unique clusters at each epsilon.
  • clusters_%(epsilon)s.csv: "clusterId, document id" for all documents that are in clusters at the epsilon specified by the file name.
  • joined_cluster_labels.csv: "clusterid,docid,epsilon" for each document at every epsilon level at which the document has a cluster.
• Sentences
  • fragment_summaries.csv: "docid,sent_id,sentnum" for every sentence where the sentnum is the zero-based index of the sentence within the document. A sentnum of "0" means that it is the first sentence in the document. A sentnum of "1" means that it is the second sentence in the document. Etc.
  • fragment_metadata.csv: "docid,date,domain" for every document referenced by fragment_summaries.csv. Date is the web crawler scrape date.
  • sentence_id_lookup.csv: "text, sentence_id" for every unique sentence.
• News/Domain Graph outputs
  • domain_graph_sentence_count.csv: "Source,Target,Weight"
    • Source is a domain that has news articles
    • "Target" is a domain that has news articles that were copied by the "Source" domain.
    • "Weight" is the number of sentences that "Source" copied from "Target".
  • sentence_origin.csv: "sent_id, domain" where the domain is the internet domain where the sentence was first seen by the crawler.
  • domain_graph.csv: "Source,Target,Weight"
    • Source is a domain that has news articles
    • Target is a domain that has news articles that were copied by the "Source" domain.
    • Weight is the number of documents that "Source" copied from "Target".