This system evaluates a series of mementos (archived web pages) to determine which are off topic. The series can be part of an Archive-It collection, a single TimeMap, or stored in a WARC file. For more information about memento and TimeMaps, see http://timetravel.mementoweb.org/about/ and https://tools.ietf.org/html/rfc7089. For more information about WARC files, see http://archive-access.sourceforge.net/warc/. For information about Archive-It, see https://archive-it.org.
To download the latest image corresponding with a stable release of this code
docker pull shawnmjones/offtopic-archive-analysis:latest
Then start the docker image in the background
docker run -td shawnmjones/offtopic-archive-analysis --name offtopic
To analyze a collection, run a command like the following. The -i and -o options are mandatory.
docker exec off-topic python detect_off_topic.py -i archiveit=3936 -o outputfile
Analysis is done one of several input types supplied by the -i option on the command line. Information about off-topic mementos are stored in a file specified by the -o option.
To analyze a given Archive-It collection use the -i option with the word archiveit and the collection number supplied after an = sign, like so:
docker exec off-topic python detect_off_topic.py -i archiveit=3936 -o outputfile
To analyze a TimeMap use the -i option with the word timemap and the TimeMap URI supplied after an = sign, like so:
docker exec off-topic python detect_off_topic.py -i timemap=http://wayback.archive-it.org/3936/timemap/link/http://www.doi.gov/index.cfm -o outputfile
Multiple TimeMap URIS can be supplied separated by commas without a space.
To analyze WARC files, use the -i option with the word warc and the WARC file names supplied after an = sign, separated by commas without spaces, like so:
docker exec off-topic python detect_off_topic.py -i warc=warc1.warc.gz,warc2.warc.gz -o outputfile
Finally, if you have already downloaded data using this tool into a directory, you can supply that as well:
docker exec off-topic python detect_off_topic.py -i dir=/tmp/working -o outputfile
This software contains several different measurement algorithms for analysis. So far, they are as follows:
- bytecount - A comparison of the percentage of bytes that changed between the first memento in TimeMap and the other mementos from that TimeMap.
- wordcount - Like bytecount, but with words instead of bytes.
- jaccard - The Jaccard distance between the first memento and the other mementos in each TimeMap in a collection.
- cosine - The cosine similarity of the first memento with the other mementos in each TimeMap in a collection.
- tfintersection - The difference between the top 20 terms of the first memento compared to the other mementos in each TimeMap in a collection.
By default, the system uses the cosine and wordcount algorithms with thresholds of 0.15 and -0.85, respectively.
Algorithms can be supplied on the command line using the -m option along with the name of the algorithm and an optional threshold specified after an =:
docker exec off-topic python detect_off_topic.py -i archiveit=3936 -o outputfile -m jaccard=0.10,cosine=0.20
One can also specify:
-lto specify a log file (by default the application logs to stdout)-dto change the working directory where data is downloaded and processed (by default /tmp/working)-vto enable extra debugging statements in the log file
- Python 3.6
- java 1.7+
To install the Python prerequisites:
pip install -r requirements.txt
python -m nltk.downloader punkt
To install boilerpipe library:
git clone https://github.com/misja/python-boilerpipe.git
cd python-boilerpipe
wget https://storage.googleapis.com/google-code-archive-downloads/v2/code.google.com/boilerpipe/boilerpipe-1.2.0-bin.tar.gz
python setup.py install
Your feedback is always welcome. You can send me an email on sjone@cs.odu.edu or open an issue on github.