As most social marketing professionals know, the signal-to-noise ratio in social media streams can be awful. For example:
- By some estimates, over half of Twitter accounts are either ciphers or bots.
- Content farms, auto-blogs, tweetbots and splogs continue to churn-out duplicate content at an amazing rate.
- Over 90 percent of social media users have experienced spam.
- The Wall Street Journal recently reported that spam makes up about 1.5% of Twitter posts and 3-4% of Facebook posts.
- Social media is rife with user-duplicated content caused by cross-posting, retweeting and forwarding.
Yet even in the face of these facts, we continue to ingest and process data streams without filtering for quality, and then make decisions based upon the data we extract – even though we know the quality is suspect. Should we really keep making decisions this way?
Noise is also a serious time waster. As any social community manager will tell you, social media noise can be a real productivity issue for anyone involved in social media marketing, customer service and social media outreach. And filtering takes time: most social managers are not linguistics experts, so getting their filters right takes a lot of trial and error using the relatively crude keyword filters available in today’s leading social media monitoring systems (SMMS).
What’s That Noise?
Noise in social media has three main sources:
- fake/duplicate accounts: a large number of social media accounts are not owned or managed by a real person. Fake accounts mess with your reach and follower/friend statistics.
- spam: unsolicited & poorly targeted marketing messages, phishing scams and malware injection attempts. Impacts your sentiment analytics and keyword/brand trends.
- duplicate content: splogs, content farms, autoblogs, tweetbots, retweeting, cross-posting, forwarding, et al. Can seriously skew your reach and mention statistics.
Unfortunately, most leading social media analytics and social media monitoring software (SMMS) vendors do not offer effective filters to protect you from the issues above. And you won’t find social media gurus and strategists talking about the data quality issue – probably because (a) no one has offered a bullet-proof solution; (b) it’s a boring topic; or, (c) this is technical problem that most marketing-trained social strategists simply aren’t trained/experienced to address.
Solutions Are On The Way
The good news is that there are many efforts underway to help businesses extract more signal from social media streams. For example,
- influence scoring systems such as PeerIndex, Klout and Kred make it easier to weed-out less influential accounts from your analysis and engagement streams. Unfortunately, they also weed-out new accounts of people who are forming their initial impression of what social media – and you – can do for them.
- Twitter has made huge strides in controlling spam since they started focusing on the problem in 2008, reducing the spam rate from over 20% to less than 2% today. They currently have a team of over ten specialists who work full time to combat spam and user security threats. In January, 2012, they acquired Dasient, a leading real time web security company.
- Facebook is currently blocking over 200 million malicious actions each day. A site integrity team of 30 engineers exists, and another team of 46 people works on site security. Yet another team of 300 deals with user issues.
- consultancies and agencies with platforms have added advanced filtering capabilities to their service offerings. Converseon and Dachis Group are good examples.
- intelligent real time data processing vendors such as OpenAmplify, Solariat and Datasift provide data processing services that can be configured to filter your streams – if you have a detailed understanding of the language in the domain you are analyzing. And, you will need to get their data into a business application to use it.
- “insight as a service” providers (credit to Evangelos Simoudis for the label) have arrived on the scene, including Acteea, 9Lenses, JBara and Totango. Data analysis SaaS providers Host Analytics, 8thbridge and Dachis Group have also created insight analysis offerings to complement their existing software solutions.
- SMMS vendors Visible and Attensity have proven text processing and sentiment analysis baked into their offerings. Radian6 recently added Insights, which offers a range of third party semantic processing and NLP filtering add-ons for their analytics package.
- need-detection services such as NeedTagger are helping non-technical marketing and customer service professionals see past the noise to find people in need for their products, services and content. These vendors combine next-generation language filters, user-friendly applications and marketing analytics into a single business service.
Don’t Wait To Begin Filtering
With over 1 billion social media accounts attracting the interest of spammers and content farms, companies must start filtering and paying attention to the quality of their data soon – or “social media analytics” could become a bit of a joke. In addition, social media desks will continue to swell in size until noise can be driven out of feeds on a continuous improvement basis.
Bottom-line, noise filtering technology improves the quality of your marketing decisions and helps your social media teams stay focused on the highest-quality opportunities.
What is your company doing to reduce the noise in your social media streams?