Datasets:

slnader
/

fcc-comments

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----
-annotations_creators:
-- expert-generated
-language:
-- en
-language_creators:
-- found
-license:
-- cc-by-nc-sa-4.0
-multilinguality:
-- monolingual
-pretty_name: fcc-comments
-size_categories:
-- 10M<n<100M
-source_datasets:
-- original
-tags:
-- notice and comment
-- regulation
-- government
-task_categories:
-- text-retrieval
-task_ids:
-- document-retrieval
----
-# Dataset Card for fcc-comments
-## Table of Contents
-- [Table of Contents](#table-of-contents)
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-## Dataset Description
-- **Repository: https://github.com/slnader/fcc-comments **
-- **Paper: https://doi.org/10.1002/poi3.327 **
-### Dataset Summary
-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?
-`fcc-comments` is an annotated version of the comment corpus from the Federal Communications Commission's
-(FCC) 2017 "Restoring Internet Freedom" proceeding. The source data were downloaded directly from the FCC's Electronic
-Comment Filing System (ECFS) between January and February of 2019 and include raw comment text and metadata on
-comment submissions. The comment data were processed to be in a consistent format
-(machine-readable pdf or plain text), and annotated with three types of information: whether the comment was cited in the
-agency's final order, the type of commenter (individual, interest group, business group), and whether the comment was associated with an in-person meeting.
-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).
-## Dataset Structure
-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. |
-### Data Fields
-The following tables are available in fcc.pgsql:
-- comments: plain text comments associated with submissions
-| column      | type | description |
-| ----------- | ----------- | ----------- |
-| comment_id      | character varying(64)       | unique id for plain text comment |
-comment_text | text | raw text of plain text comment
-row_id | integer | row sequence for plain text comments
-- submissions: metadata for submissions
-| column      | type | description |
-| ----------- | ----------- | ----------- |
-submission_id   | character varying(20)  | unique id for submission
-submission_type | character varying(100) | type of submission (e.g., comment, reply, statement)
-express_comment | numeric                | 1 if express comment
-date_received   | date                   | date submission was received
-contact_email   | character varying(255) | submitter email address
-city            | character varying(255) | submitter city
-address_line_1  | character varying(255) | submitter address line 1
-address_line_2  | character varying(255) | submitter address line 2
-state           | character varying(255) | submitter state
-zip_code        | character varying(50)  | submitter zip
-comment_id      | character varying(64)  | unique id for plain text comment
-- filers: names of filers associated with submissions
-| column      | type | description |
-| ----------- | ----------- | ----------- |
-submission_id | character varying(20)  | unique id for submission
-filer_name    | character varying(250) | name of filer associated with submission
-- documents: attachments associated with submissions
-| column      | type | description |
-| ----------- | ----------- | ----------- |
-submission_id   | character varying(20) | unique id for submission
-document_name   | text                  | filename of attachment
-download_status | numeric               | status of attachment download
-document_id     | character varying(64) | unique id for attachment
-file_extension  | character varying(4)  | file extension for attachment
-- filers_cited: citations from final order
-| column      | type | description |
-| ----------- | ----------- | ----------- |
-point           | numeric                | paragraph number in final order
-filer_name      | character varying(250) | name of cited filer
-submission_type | character varying(12)  | type of submission as indicated in final order
-page_numbers    | text[]                 | cited page numbers
-cite_id         | integer                | unique id for citation
-filer_id        | character varying(250) | id for cited filer
-- docs_cited: attachments associated with cited submissions
-| column      | type | description |
-| ----------- | ----------- | ----------- |
-cite_id       | numeric               | unique id for citation
-submission_id | character varying(20) | unique id for submission
-document_id   | character varying(64) | unique id for attachment
-- near_duplicates: lookup table for comment near-duplicates
-| column      | type | description |
-| ----------- | ----------- | ----------- |
-target_document_id    | unique id for target document
-duplicate_document_id | unique id for duplicate of target document
-- exact_duplicates: lookup table for comment exact duplicates
-| column      | type | description |
-| ----------- | ----------- | ----------- |
-target_document_id    | character varying(100) | unique id for target document
-duplicate_document_id | character varying(100) | unique id for duplicate of target document
-- in_person_exparte: submissions associated with ex parte meeting
-| column      | type | description |
-| ----------- | ----------- | ----------- |
-submission_id   | character varying(20) | unique id for submission
-- interest_groups: submissions associated with interest groups
-| column      | type | description |
-| ----------- | ----------- | ----------- |
-submission_id | character varying(20) | unique id for submission
-business      | numeric | 1 if business group, 0 otherwise
-## Dataset Creation
-### Curation Rationale
-The data were curated to perform information retrieval and summarization tasks as documented in https://doi.org/10.1002/poi3.327.
-### Source Data
-#### Initial Data Collection and Normalization
-The data for this study come from the FCC's Electronic Comment Filing System (ECFS) system, accessed between January and February of 2019.
-I converted the API responses into a normalized, relational database containing information on 23,951,967 submissions.
-23,938,686 "express" submissions contained a single plain text comment submitted directly through the comment form.
-13,821 "standard" submissions contained one or more comment documents submitted as attachments in various file formats.
-While the FCC permitted any file format for attachments, I only consider documents attached in pdf, plain text, rich text,
-and Microsoft Word file formats, and I drop submitted documents that were simply copies of the FCC’s official documents (e.g., the NPRM itself).
-Using standard OCR software, I attempted to convert all attachments into plain text and saved them as machine-readable pdfs.
-#### Who are the source language producers?
-All submitters of public comments during the public comment period (but see note on fake comments in considerations).
-### Annotations
-#### Annotation process
-- Citations: I consider citations from the main text of the FCC's final rule. I did not include citations to
-  supporting documents not available through ECFS (e.g., court decisions), nor did I include citations
-  to submissions from prior FCC proceedings. The direct citations to filed submissions are included
-  in a series of 1,186 footnotes. The FCC’s citation format typically followed a relatively standard
-  pattern: the name of the filer (e.g., Verizon), a description of the document (e.g., Comment), and
-  at times a page number. I extracted citations from the text using regular expressions. Based on a
-  random sample of paragraphs from the final order, the regular expressions identified 98% of eligible citations,
-  while successfully excluding all non-citation text. In total, this produced 1,886 unique citations.
-  I then identified which of the comments were cited. First, I identified all documents from the cited filer
-  that had enough pages to contain the page number cited (if provided), and, where applicable, whose filename
-  contained the moniker from the FCC’s citation (e.g., "Reply"). The majority of citations matched to only one
-  possible comment submitted, and I identified the re- maining cited comments through manual review of the citations.
-  In this way, I was able to tag documents associated with all but three citations. When the same cited document was
-  submitted under multiple separate submissions, I tagged all versions of the document as being cited.
-- Commenter type: Comments are labeled as mass comments if 10 or more duplicate or near-duplicate copies were
-  submitted by individual commenters. Near-duplicates were defined as comments with non-zero identical information scores.
-  To identify the type of commenter for non-mass comments, I take advantage of the fact that the vast majority of organized
-  groups preferred standard submissions over express submissions. Any non-mass comment submitted as an express comment was
-  coded as coming from an individual. To distinguish between individuals and organizations that used standard submissions,
-  I use a first name and surname database from the names dataset Python package to characterize filer names as belonging to
-  individuals or organizations. I also use the domain of the submitter’s email address to re-categorize comments as coming
-  from organizations if they were submitted on behalf of organizations by an individual. Government officials were identified by
-  their .gov email addresses. I manually review this procedure for mischaracterizations. After obtaining a list of organization
-  names, I manually code each one as belonging to a business group or a non-business group. Government officials writing in
-  their official capacity were categorized as a non-business group.
-- In-person meetings: To identify which commenters held in-person meetings with the agency, I collect all comments labeled
-  as an ex-parte submission in the EFCS. I manually review these submissions for mention of an in-person meeting. I label
-  a commenter as having held an in-person meeting if they submitted at least one ex-parte document that mentioned an in-person meeting.
-#### Who are the annotators?
-Annotations are a combination of automated and manual review done by the author.
-### Personal and Sensitive Information
-This dataset may contain personal and sensitive information, as there were no restrictions on what commenters could submit to
-the agency. This dataset also contains numerous examples of profanity and spam. These comments represent what the FCC decided was
-appropriate to share publicly on their own website.
-## Considerations for Using the Data
-### Discussion of Biases
-This proceeding was famous for the large number of "fake" comments (comments impersonating ordinary citizens) submitted to the
-agency (see [this report](https://ag.ny.gov/sites/default/files/oag-fakecommentsreport.pdf) by the NY AG for more information).
-As such, this comment corpus contains a mix of computer-generated and natural language, and there is currently no way to reliably separate
-mass comments submitted with the approval of the commenter and those submitted on behalf of the commenter without their knowledge.
-## Additional Information
-### Licensing Information
-CreativeCommons Attribution-NonCommercial-ShareAlike 4.0 International.
-### Citation Information
-```
-@article{handan2022,
-  title={Do fake online comments pose a threat to regulatory policymaking? Evidence from Internet regulation in the United States},
-  author={Handan-Nader, Cassandra},
-  journal={Policy \& Internet},
-  year={2022}
-}
-```

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