askcomm: Python 3 module - Search patterns for event-based, community-detected twitter data.
By Chris Lindgren firstname.lastname@example.org
Distributed under the BSD 3-clause license. See LICENSE.txt or http://opensource.org/licenses/BSD-3-Clause for details.
A set of search patterns that query a corpus of event-based and community-detected tweets, but it could be modified to query most social-network (node-edge) data. The queries are great for content produced within the detected-community subgraph data.
It assumes you have:
- imported your corpus as a pandas DataFrame,
- included metadata information, such as a list of dates and list of groups to reorganize your corpus, and
- pre-processed your documents as community-detected data across periodic events.
query_controller: Accepts corpus and hub user data and searches for tweets germane to the detected module community across a range of periods and communities. It uses the
find_mentions function to conduct a cross-reference search within a period’s data range with 2 options: ‘mentions_only’ or ‘user_and_mentions’. ‘
mentions_only’ searches a column with a List of mentions per tweet. ‘
user_and_mentions’ cross references the author of a tweet with the list of mentions. It returns a Dict of top result tweets found during that period.
query_controller( hubs=df_hubs,#community-detected data hub_col_period='period',#column name for periods hub_col_module='info_module',# column name for community name hub_col_users='name',#column name for period_range=[1,10],#range of desired periods module_range=[1,10],#range of desired communities/modules corpus=c_htg,#content corpus period_dates=period_dates,#List of lists with dates to col_dates='dates'#column name for dates )
convert_to_df: Converts the Dict output from query_controller into a Dataframe with top result per user. If no tweet found , appends as None.
find_ht: Queries subset of isolated mentioned or authored tweets with hashtag group list. It returns another subset as a dataframe.
find_links: Queries links in tweets with search string. It returns subset as a dataframe.
Other functions include:
It functions only with Python 3.x and is not backwards-compatible.
Warning: askcomm performs little to no custom error-handling, so make sure your inputs are formatted properly. If you have questions, please let me know via email.
- Download this repo onto your computer.
- Store the folder in a meaningful location.
- Open a terminal.
- In the terminal, navigate to the root of the folder.
- In the terminal, run
pip install .