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Most of the online search is keywords-based. Same in social domain, a vast number of analytical tools, networking platforms and mobile apps use keyword-based technologies as well.

There is a difference, of course, between traditional internet search and social search. The former finds websites. The latter finds conversations, messages, posts. Keyword-based internet search is doing a decent job for us for over 20 years. Keyword-based social search is not doing a decent job at all.

Consider a basic example: finding on Twitter who is interested in buying jeans. We can start by typing ‘jeans’ but that brings up too much noise. Maybe ‘need jeans’? Less noise but then we  people who use expressions like ‘looking for jeans’ or ‘want jeans’ or shopping for jeans’. Not to mention those who use ‘denim’, or brand names. So we have to run multiple searches or create a complex search string using logical AND and OR and hope it works. Neither option is simple, or convenient, and certainly not efficient.

The above example highlights the major flaw with keyword search - it does not capture the meaning of social conversations, and therefore cannot be a reliable source of information about conversations.

It does not provide too much of correct information. And it does provide lots of incorrect information. But the biggest problem is that it has extremely limited potential for improvement.  

So as long as we stick with keyword-based social search the results are destined to be limited.

Why, then, we stick with keyword-based search in social search? Simply because there is no good alternative. Until recently, that is.  

The advanced semantic technologies capable of capturing the meaning, or intent, of conversations are now offering an exciting alternative.

I will discuss these technologies on my next blog.

There is a huge amount of information out there. And it is growing. To make it efficient and increase our competitive advantage we need to evolve and start using information in a smart way, by concentrating on data that drives business value because it is accurate, actionable, and agile. Accuracy is an important measure that determines the quality of data processing solutions.

How accuracy is calculated?

It is easy to do with structured data, because the requirements are formalizable. It is less obvious with unstructured data, e.g. a stream of social feeds, or any data set that involves natural language. Indeed, the sentences of natural language are subject to multiple interpretations, and therefore allow a degree of subjectivity. For example, should a sentence ‘I haven’t been on a sea cruise for a long time’ be qualified for a data set of people interested in going on a cruise? Both answers, yes and no, seem valid.

In these cases an argument was put forward endorsing a consensus approach which polls data providers is the best way to judge data accuracy. This approach essentially claims that attributes with the highest consensus across data providers is the most accurate.

At nmodes we deal with unstructured data all the time because we process natural language messages, primarily from social networks. We do not favor this simplistic approach, as it is considered biased, inviting people to make assumptions based on what they already believe to be true, and making no distinction between precision and accuracy. Obviously the difference is that precision measures what you got right, and accuracy measures both what you got right and what you got wrong. Accuracy is a more inclusive and therefore more valuable characteristic.

Our approach is

a) to validate data against third party independent sources (typically of academic origin) that contain trusted sets and reliable demography. Validating nmodes data against third party sources allows us to verify that our data achieves the greatest possible balance of scale and accuracy.

b) to enrich upon the existing test sets by purposefully including examples ambiguous in meaning and intent, and providing additional levels of categorization to cover these examples.

Accuracy is becoming important when businesses move from rudimentary data use, typical of the first Big Data years, to a more measured and careful approach of today. Understanding how it is calculated and the value it brings helps in achieving long-term sustainability and success.