Bill Fischer of TwitterJobSearchTwitterJobSearch is a natural language search engine that reviews all of the tweets about jobs to identify which ones are offers of employment, and lists them on its site.

It was developed by Workdigital Ltd, a vertical search company that also founded both workhound.co.uk. I’ve been talking to co-founder Bill Fischer about TwitterJobSearch…

How does TwitterJobSearch work?

We are a search technology company, and
our speciality is natural language search. A year ago, we thought it
would be interesting to run millions of tweets through our search
engine to see if it would work for 140 characters and if we could
distinguish between those which were about actual jobs, or which were
just people talking about about jobs.

It’s relatively easy to spider a full webpage with plenty of content, but more of a challenge when applied to a tweet.

With some fine tuning, we were able to
distinguish the genuine job listings from the irrelevant tweets, so a
tweet relating to a news story about job losses in the construction
industry for instance, or someone bitching about their own job, could be distinguished from a tweet that is linked to a genuine job
listing.

This is something a keyword search
can’t achieve, but natural language search can be very effective for. If
something appears to be a job offer, we follow the links from that
tweet and spider the page, grabbing the relevant data associated with
it.

So, we can see a mention of a vacant
position for a sous chef, then we can follow the links from the tweet
to the restaurant’s website, and list details of the job, the sort of
details that wouldn’t be featured in the original tweet.

Are many people listing jobs on
Twitter?

People like to multi-post positions
they are seeking to fill, and also, since it is very easy to post a
tweet, they will often mention jobs on Twitter first, so we can
sometimes get stuff that hasn’t been advertised elsewhere. To give you an idea of scale, 240,000 unique job
vacancies were tweeted on the last 30 days.

It is mainly US-focused, but we also
show jobs and get some traffic from elsewhere, especially the UK,
Canada, Australia and Germany. At the moment, we are sending about
1.4m clicks through to jobs every month, to other job listings boards, LinkedIn pages,
company websites etc.

Do people advertise jobs with you
directly?

We pick up the majority from searching Twitter, but companies can list their jobs directly with us. We are working with clients like
Kellogs, Astro Zenica and others. Some will pay to post a single job,
while we create branded job feeds and Twitter accounts for brands
like Adidas.

How have you funded / monetised the
service?

We took some Series A funding in August
of last year. Our revenue comes from advertising; it is free to have
your job indexed but we charge for featured listings on a CPC basis.

How much traffic are you getting?

User numbers have been growing, we’ve
had 150,000 unique visitors over the last 30 days, as well as more
than 100,000 followers on Twitter, which is our main source of
traffic.

How does it work?

It’s very easy to build a keyword
search engine for Twitter, but for us in practice, the hard work of
building up a library of words and phrases associates with job
vacancies to scan for.

The system pools and analyses each of
the phrases we find that may be job listings. If the system finds
false positives, then we tweak it to improve the accuracy, and the
vast majority of listing on our site are accurate.

I think people who use search engines
are unforgiving, and perhaps don’t realise the work that goes on to
produce accurate results, and can be frustrated if they see any errors in the
results.

What’s next? Will you apply the same principles to
other sectors?

Jobs is one of the biggest listings
categories, but also one of the most difficult to search and index. We intend to expand
the idea into other areas. We are currently doing tests for
automotive and real estate listings. Automotive is easier to search
for as there are fewer possible attributes associated with cars for
sale, price, number of doors, mileage etc.