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Social Media Analytics a Survey of Techniques Tools and Platforms

Social media analytics has emerged as an important subject of research in public websites and online services. This survey is intended to give an overview of available techniques, tools, and platforms supporting social media analysis. The survey starts with describing the evolution of social media analysis techniques, tools and platforms. An overview on various techniques used in various aspects of social data analysis like volume, velocity and variety is presented covering text mining and sentiment analysis using big data analytics frameworks. The survey further provides an insight on features of available social media analytic platforms and their implementation requirements.

This book covers the evolving field of social media analytics, which focuses on turning content published by users into valuable insights for businesses. It explores the basic techniques, tools and platforms available, as well as some of the mature applications that allow real-time image, video and text analyses enabling fast and effective decision making.

Social Media Analytics Tools

Social media analytics tools are essential in running a successful social media campaign. It allows social media experts to track and determine the performance of various portions of the social marketing campaign such as sales, customer service and sentiment analysis.

In terms of sales, these tools show how well a social media marketing campaign is going by showing all positive turnovers or purchases that come directly from social media sources such as Facebook and Twitter. These sites are useful for disseminating purchase or signup links and correlate directly to traffic which can be picked up by a specific social media analytics tool. For brand recognition and sentiment analysis, some tools are able to mine the data from social networking sites in order to find or discover the sentiment of people towards a brand or business through methods such as natural language processing and pattern recognition.

Common methods of analysis used:

  • Data mining
  • Transformation
  • Natural language processing
  • Data pre-processing
  • Data visualization
  • Hidden pattern evaluation
  • Traffic analysis

Examples of social media analytics tools or platforms:

  • Google Analytics
  • Twitter Analytics
  • Facebook Insight
  • Hootsuite

What Is Social Media Analytics?

Techopedia defines social media analytics thusly:

“Social media analytics (SMA) refers to the approach of collecting data from social media sites and blogs and evaluating that data to make business decisions. This process goes beyond the usual monitoring or a basic analysis of retweets or ‘likes’ to develop an in-depth idea of the social consumer.”

This is a pretty apt description, though we’d like to clarify that “social media sites” encompasses not just Facebook, Twitter, and the like, but forums and review sites as well as blogs and news outlets. Really, it’s anywhere that consumers can share their beliefs, opinions and feelings online.

Just as buzzwords lose meaning over time, many brands lose sight of the value of social media analytics because at first glance social data comes with a lot of noise. Nobody has time to sort through results that include spam, bots, and trolls to get to the good stuff.

Additionally, brands often make the mistake of running a social media analysis on a topic once and then call it good. Online is always in a state of flux, so there is an ongoing relationship with the data in social media analytics to account for fluctuations inherent in the medium.

The ability to cut through the online noise in pursuit of actionable market, competitive and consumer intelligence, coupled with consistent monitoring to track conversational fluctuations over time is the mark of effective social media analytics.

Quite simply, when you have state of the art tools, social media analytics becomes a treasure trove of consumer insights you can’t find anywhere else. Without them however, social media presents a guessing game in an ever-changing slog of information without cohesive insight.

Building on this, we’d extend the definition above to say social media analytics is a collection of data unearthed via multiple techniques from multiple sources versus a single tool in and of itself.

To clarify, let’s run through some terms often confused with social media analytics.

So-Called Synonyms That Aren’t

If social media analytics is a destination, what tools contribute to the journey? And what are their distinctions.

Social Media Intelligence is the closest term-cousin to social media analytics. Social intelligence represents the stack of technology solutions and methods used to monitor social media, including social conversations and emerging trends.

This intelligence is then analyzed and used to create meaningful content and make business decisions across many disciplines.

Social Media Listening is one of the terms most often confused with social media analytics. But social listening applies to one specific aspect of social media analytics: Learning about your audience.

The goal here is to uncover what they love, hate, and love to hate – as opposed to any assumptions you may have. It’s about getting to know them as people, not just prospects.

For instance, if you want to know what people in Boston have to say about pizza, you can find out using a tool like NetBase Pro. From there, you can look for additional common ground to create audience segments to make your interactions more personal.


Social Media Monitoring is the second term most often confused for social media analytics. It’s also thought to be synonymous with social listening, but the two are very different.

Social monitoring focuses on following social audiences to be alerted to spikes in activity that present either an opportunity you wouldn’t want to miss, or a potential disaster you want to avoid. It’s about seeing posts like this in time to respond and avoid a viral crisis:


Social Competitive Analysis is the process of investigating competitors of your brand and their audience. Because social media is such a transparent medium, social media analytics tools can be applied to brands beyond your own.

This gives you the advantage of seeing how they serve their customers, what consumers love or hate about them and what new products or services they’re offering.

This information allows you to see what your shared audience gets excited about, so you can capitalize on fresh ideas you might never have had yourself. Additionally, it can save the day when things go wrong, or save your own budget by learning from competitors’ mistakes.

And as consumer attitudes are never static, brands can also monitor how other brands are handling the social climate to adjust if things are hitting close to home. Earlier this summer Quaker Oats retired its longstanding Aunt Jemima brand out of concern for racial impacts to consumers. Others are paying attention.


Image Analytics is a new feature made possible by the evolution of social media analytics technology. Image analytics levels up text analysis by identifying scenes, facial expressions, geographical locations, brand logos and more in social images. This is especially useful when a brand is pictured, but not explicitly mentioned in the text.

As social users become increasingly visual, the inability to perform image analytics becomes a deal-breaker when researching social media analytics tools. Basically, if you’re social media analytics tool isn’t picking up images where your brand is pictured, but not explicitly mentioned, then you’re missing out on a lot of the conversation.

And to really make sure you’re not missing a thing, it needs to not only capture full logos, but altered, partial or reversed brand mentions as well – like the reversed and cut-off PetSmart logo below.

Social Media Sentiment Analysis

Social sentiment is the tie-in that applies to all facets of your social media analytics. Without it, you don’t have any way of gauging why you’ve suddenly got 500K more “likes” or shares than usual. What if an uptick in activity isn’t a good thing? The only way to know is through sentiment analysis.

This layer of social media analytics uses Natural Language Processing (NLP) to understand whether social conversations are positive or negative, and to measure the strength of those emotions. This helps you triage responses so you don’t waste energy on posts that don’t matter, while ignoring posts that do.consumer-emotions

Emotions when talking about dogs on social – the “love” is strong

Customer Experience Analytics combines social listening insights with Voice of the Customer (VoC) verbatims like surveys, reviews, website feedback, chat messages, market research, and data from internal systems like call center, help center, and web support collected via CRM tools.

This additional data can be brought into your social media analytics to give you a comprehensive understanding of your customers across all touchpoints.

And speaking of understanding consumers, Quid Social is the social media analytics tool that excels in helping brands do just that.

complete guide to sma

Quid Social Ups the Ante in Social Media Analytics

Quid Social melds seamlessly with your NetBase social media analysis and spreads it out like a map, offering contextualized social insights at a glance. In other words, it’s your social media topic – visualized.

This symbiotic relationship is great because using disparate data analysis tools or sources is an unnecessary headache that you don’t have to deal in gaining actionable intelligence from your data.

If your tools are clunky, cumbersome or tiring, then chances are you’ll miss something in the gaps between tools, or from sheer frustration. Quid Social solves the problems that users face when stitching their social media analysis together from disconnected sources by offering a one-and-done solution. In other words, it’s a next-level social media analytics tool that synthesizes your data to establish a cohesive and easily understandable window into the online narrative.

In the same manner that Quid Pro allows users to parse company, patent, or news and blogs datasets, Quid Social uses the same interface to deep dive into any social media topic to extract insights from the returned data to inform your brand’s decision makers.

This is accomplished through next generation artificial intelligence (AI) driven social media datasets that provide a 360-degree contextualized view of the social narrative on any topic. And no matter how niche your topic may be, someone is out there talking about it online. Quid Social mines the depths of all social media platforms, consumer reviews, forums and much more – ensuring you capture it all.

And not having to transfer your data from one tool to another saves time and energy, which translates directly to your bottom line. It also offers in depth social coverage that enables Quid users to make smarter, faster data driven decisions for their business, bypassing the bloat of traditional social media analytic tools.

Quid Social allows users to not only analyze social conversations, but discover emerging trends and themes, parse out key opinion leader (KOL) narratives, analyze and monitor competitors and evaluate social media influencer performance – just to name a few. The insights gained in these areas can boost your balance sheet by way of the speed in which brands are able to make strategic business decisions; allowing brands to pivot to avoid pitfalls, spot market white space and stay a step ahead of the competition.

Additionally, Quid Social visualizes the main drivers of social conversations allowing you to see the interconnectivity between adjacent social sub-conversations. This visualization of the online narratives allows users to quickly understand the angle from which target audiences are talking about a topic or issue.


Social Media Analytics offers a complete reference guide to the emerging field of social media analytics, which over the last decade has emerged as one of the most important research areas in business intelligence, marketing, and academic disciplines. Designed for a broad audience of both practitioners and researchers, this book covers the basic foundations of social media analytics and presents emerging topics regarding data mining, machine learning, natural language processing, text mining and opinion mining in social media analytics.

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