How Twitter leads social media data?
Any social-media analytics initiative needs to be comprehensive and sleek; otherwise, it can end up being very expensive and incredibly time consuming. Here, the word “sleek” isn’t referring to fancy dashboards or graphics, but to good, clean data. The priority for social enterprises and networkers needs to be the quality of the data and the trustworthiness of the analytics.
This means complete sets of data that can be manipulated and filtered into categories like content and location, and even narrowed down to specific individuals. There also needs to be access to historical postings, possibly going back years. Everything needs to done in a suitably fast period of time, and within context. Most importantly, you need the proper analytical tools to use your collected data profitably. And now, Twitter can help make all that easier for you.
The Twitter Certified Products Program
Twitter has just launched its “Certified Products Program”; a partnership with twelve companies that is intended to make Twitter “more valuable to businesses”, according to the guidelines for certification into the program.
Some other guidelines include: solving a problem that Twitter does not already address, bringing Twitter to new or lacking markets, and encouraging meaningful engagement with the Twitter network. The move will, in theory, benefit data-focused Twitter consumers and promote a data ecosystem full of high-grade supporting services and products.
The partners in the program should fit into one of three categories: Tools for engagement, tools for analytics and data resellers. This means you have to help publish on Twitter, help to measure and learn from the actual content on Twitter, or help to tweet large masses of people.
The difficulty of harvesting and computing social data
Due to the large cost of harvesting social-data, it is typically out of reach for most businesses. This creates a niche for the data resellers in the program, namely, Gnip, DataSift, and Topsy. Social-data analysis is also, for the most part, beyond the capabilities of average businesses. The complicated process of applying NLP processing algorithms (to identify sentiment, entities, and relationships within online and social text) requires very specialized software. There is a very clear need for cheaper but tidy social data.
Whether you’re looking at extracting emotion, intent, or polarity, a key element in social-intelligence is sentiment. The patterns can be detected across multiple or single data sources, and in the analysis of the situation and context that guides the decision making process. However, considerations go beyond this, and also extend into the integration of socially-derived data.
Large, broad amounts of data are required, so quality data must be pulled from as many available sources as possible. This can then be applied to media analysis, market research, public affairs, financial markets, and even the customer experience. This opens up whole new possibilities for operations and increases our ability to gather business intelligence.
This is not, however, an automated process. Human judgment is still required to select data, train tools, suggest processes, and verify and apply insights. There is not yet a substitution for human involvement in this process.
Take the opportunity to learn from your peers and industry experts, whenever the chance presents itself. This will help you learn to listen and analyze; something that is essential for researching the customer experience and marketing impact studies. Push for the highest level of social-intelligence understanding and set clear goals.
What about LinkedIn and Facebook social data ?
It is still uncertain whether or not other social media platforms like LinkedIn and Facebook will be able to launch the same type of program. Not only are the businesses structured differently, but the technical architectures of the sites are different from that of Twitter’s.
It would be extremely beneficial to their customer’s, however, if they were able to find a way to run similar programs to that of the Certified Products Program. Overall, it would improve data quality, simplify social business decisions, and simplify analysis. Twitter is well on its way to standardizing the use of data within social media.
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