Blueprint for extending the message parsing framework

The inbound message parsing framework was developed during GSoC 2012. See the 2012 GSoC message parser project.

• The framework is highly extensible and the parsing workflows are customisable per deployment in the templates. A nice example of this is the NLTK synonym matching filter developed during the H4D2 hackathon.(See ​here).

• The system supports multiple communication channels i.e Emails, SMS and Twitter.But, certainly a number of incoming feeds (so not just SMS/Tweets, but also RSS feeds, etc.) can be integrated with the system.So, plugging in the RSS feeds would be one useful step.

• Things that we want to extract and are essential requirements for the framework are discussed below.

Data Model Changes

• Make msg_message a Super Entity
• Each Channel would have an instance of this super entity which acts as the InBox and/or OutBox as-appropriate for that instance type
• The 'Master Message Log' then becomes the view of the super-entity (rather than having to copy messages here)
• Move non-core fields to component tables so that the core tables are uncluttered & fast

Input Source Improvements

Reliability/trustworthiness of the message sources/senders

• Currently, this is done manually through the CRUD interface with the msg_sender data model.

• A 'river' of messages are processed with starring of senders & adding of keywords on the fly so that the system gradually becomes more automated through the process.

• We could as well pre-populate the keywords database with the most frequently used keywords (esp. in incident reporting) and the rest can be added on the fly.

Parser Improvements

Topic Detection

• KeyGraph is used to detect topics across tweets/other feeds to filter relevant and actionable information from the rest of them. This is done after doing a loose filtering of information based on keywords and location.
• See ​http://keygraph.codeplex.com/ .

Actionability

• Is this something that we can actually do something with?
• Its important to manage the content coming from various message sources and separate the ones that are actionable and contains useful information from the rest of them.

"Whom Should I Follow? Identifying Relevant Users During Crises":

• ​http://www.public.asu.edu/~huanliu/papers/ht2013.pdf

Location

• Another important requirement is to improve the ability to extract location data out of unstructured text and make sense of ambigous locations.

• An OpenGeoSMS parser already exists in the default parser template(also available as an API within s3msg.py) which is able to parse lat-lon information of the location from OpenGeoSMS formatted messages. But , it would be great if it could be linked with the database (look the location up from the database).

UI Improvements

• Implementing a UI which prioritises message parsing for starred senders is a useful requirement.
• The user should be able to *star* senders and *mark* keywords through the UI.
• Possible inspirations:
  • dataLists with Filters (like TweetDeck):
  • ​http://tldrmpdemo.aidiq.com/eden/default/index/updates
  • S3Profile:

​http://tldrmpdemo.aidiq.com/eden/org/organisation/1/profile

• S3Summary:

​https://sahana.mybalsamiq.com/projects/sahanacommunityresiliencemappingprojectfinal/naked/Risk+Summary?key=ff49e93ddf8139e5eb61065660c796caa6f95845 ​http://i.imgur.com/jjaDmQ1.png ​http://twitris.knoesis.org/indiarain2013/

• Tweak the Tweet (EPIC)
• ​http://idisaster.wordpress.com/2013/05/28/more-research-on-boston-marathon-official-twitter-activity-smem/
• ​https://github.com/ushahidi/SwiftRiver
• ​http://twitcident.com
• ​http://wis.ewi.tudelft.nl/twitcident/
• Different Users:
  • Power user looking for info themselves
  • Dedicated miner (volunteer/intern/junior) mining stuff for decision makers
• Vision: be able to move between Filtered view & Firehose in gradual increments. Be able to train the automated assistants to make the filtered view more useful

(subscribe/unsubscribe)

• Features
  • See all Messages in a datatable/list across media types (FB/Twitter/RSS/YouTube/Flickr)
  • Filter them
  • Add Sender to Whitelist/Blacklist
  • Add Keyword to back-end filters
  • View Images/Video
  • Find Situation Reports
  • ReliefWeb, etc
  • Grouping/Linking results both to enhance validity & also provide a single point of entry
  • Route to other Sahana Modules
  • Drag and Drop between Raw source & Target Module
  • Mark for Action
  • create Tasks
  • create Incident Reports
  • create Assessments
  • create Situation Reports
  • Forward via Outbound Channels (Public e.g. Twitter & Private e.g. Email/SMS)
  • Semantic Search?
  • RDF Channel?

Use Cases

Parsing bounced messages

• This is very important for IFRC Africa who send out bulk emails to their volunteer base from Eden & want to know which mails are mis-typed / users moved / etc.