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README.md

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+## Description
+
+Online comment floods during public consultations have posed unique governance challenges for regulatory bodies seeking relevant information on proposed regulations. How should regulatory bodies separate spam and fake comments from genuine submissions by the public, especially when fake comments are designed to imitate ordinary citizens? How can regulatory bodies achieve both breadth and depth in their citations to the comment corpus? What is the best way to select comments that represent the average submission and comments that supply highly specialized information?
+
+We present the comment corpus from the Federal Communications Commission's (FCC) 2017 "Restoring Internet Freedom" proceeding, augmented with metadata to assist in prototyping innovative search and discovery techniques. This data release contains structured metadata and the raw text of nearly 24 million comments submitted during the proceeding. The comment data were downloaded directly from the FCC's Electronic Comment Filing System (ECFS) between January and February of 2019, processed to be in a consistent format (machine-readable pdf or plain text), and augmented with information on which comments were cited in the FCC's final order.
+
+The release also includes query-term and document-term matrices to facilitate keyword searches on the comment corpus. An example of how these can be used with the bm25 algorithm can be found [here](https://github.com/slnader/fcc-comments/blob/main/process_comments/1_score_comments.py).
+
+Please cite any use of these data as follows:
+
+Handan-Nader, Cassandra. 2022. Do fake online comments pose a threat to regulatory policymaking? Evidence from Internet regulation in the United States. Policy & Internet. [https://doi.org/10.1002/poi3.327](https://doi.org/10.1002/poi3.327)
+
+## Datasets
+
+FCC relational database (fcc.pgsql): The core components of the database include a table for submission metadata, a table for attachment metadata, a table for filer metadata, and a table that contains comment text if submitted in express format. In addition to these core tables, there are several derived tables specific to the analyses in the paper, including which submissions and attachments were cited in the final order, which submissions were associated with in-person meetings, and which submissions were associated with interest groups. Full documentation of the tables can be found in fcc_database.md.
+
+Attachments (attachments.tar.gz): Attachments to submissions that could be converted to text via OCR and saved in machine-readable pdf format. The filenames are formatted as [submission_id]_[document_id].pdf, where submission_id and document_id are keys in the relational database.
+
+Search datasets (search.tar.gz): Objects to facilitate prototyping of search algorithms on the comment corpus. Contains the following elements:
+
+| Filename      | description |
+| ----------- | ----------- |
+query_dtm.pickle | Query-term matrix (79x3986) in sparse csr format (rows are queries, columns are bigram keyword counts).
+query_text.pickle | Dictionary keyed by the paragraph number in the FCC’s Notice of Proposed Rulemaking. Values are the text of the query containing a call for comments. |
+search_dtms_express.pickle | Document-term matrix for express comments (3800691x3986) in sparse csr format (rows are comment pages, columns are bigram keyword counts). |
+search_index_express.pickle | Pandas dataframe containing unique id and total term length for express comments. |
+search_dtms.pickle | Document-term matrix for standard comment attachments (44655x3986) in sparse csr format (rows are comment pages, columns are bigram keyword counts). |
+search_index.pickle | Pandas dataframe containing unique id and total term length for standard comment attachments. |
+
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 license: cc-by-nc-sa-4.0
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