The world’s population is growing, and so is our social and digital integration. This has wide reaching implications for society, but it also creates exciting opportunities for paid social media scientists. This is in essence a giant growing data set. Any researcher, from Einstein down to a university undergrad, knows that a large in-depth data set increases the accuracy of your research. This allows you to see patterns and analyse trends in greater detail.

It takes 20 years to build a reputation and 5 minutes to ruin it. Make sure your social media management is helping not damaging your brand by reading our post "How to boost your brand reputation with social media management"

What does this mean for paid social media?

Now, many people still seem oblivious to the fact that social media platforms collect huge amounts of data on their everyday lives, especially when accounts are linked to their smartphones. You may think you’re just interacting with a great app, but in reality it’s figuring out where you go, what you do and how you shop.

A friend of mine proclaims that if you look at someone’s meta data for regular breaks in online activity, you can figure out when they’re likely to use the toilet…… but he’s weird and not allowed round the flat anymore.

When you twin this sort of data collection with an expanding socially integrated population, you end up with huge detailed data sets which can then enable all sorts of analysis. If you look at Google or any search engine, what they do is match our search terms to find relevant results. Now we have algorithms that look for terms that match to patterns in real life events - just look at predictive results next time you do a search. By matching patterns, we gain an accurate indicator of what might be expected to happen in the future.

A similar approach can be applied to paid social media. But social media has one key factor that search engine misses. Emotion.

Sentiment analysis

Sentiment analysis, or opinion mining, refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in source materials. What we like on Facebook, dislike on YouTube, Tweet about, even the emojis we use can offer an indication of what we think about a whole range of topics.

For example, if you like a post on a restaurant, sentiment analysis can tell whether you liked the place or disliked it, but it can also show the reasons why depending upon what you post (i.e. “too salty” or “bad service”). When you twin this with other data such as the cuisine, price and rating of the restaurant, we have a good picture not only of your eating habits but also of the restaurant.

This level of information gives social media analysts something else to go on in terms of predicting what we might do in the future. Combined with other data gleaned from our everyday web use, it can help analysts to predict trends. And that is something that will interest anyone from business people to political election strategists.

However, this is not an infallible practice. If we were to go purely off social media noise, Miliband would be Prime Minister, DiCaprio would have five Oscars, and tigers wouldn’t be endangered. Judging sentiment can be effective, but sometimes it doesn’t truly reflect the offline environment. Not everyone uses Twitter or Facebook, and those that do often don’t put their true feelings online.

Its already happening!

This marriage of big data to sentiment analysis is already happening and in some cases is changing how we as a species live. There are examples of it across many differing spectrums for example North American data analysts have claimed successes in using Twitter to predict significant stock market shifts involving firms such as Apple and Blackberry. Research in 2011 by Indiana University also found that Twitter sentiment was 87% accurate in predicting the short-term ups and downs of the Dow Jones Industrial Average.

In the medical world, as well as helping to predict the outbreak of infectious diseases such as flu, analysis of social media chatter can also provide an early warning system for people at risk of other conditions. The ebola outbreak of 2015 was effectively mapped using social media predictions even in areas of low internet saturation. Researchers have flagged up links between the expression of negative emotions on social media, and the risk of heart disease and mental health problems.

In the world of policing academics who analysed geographically identifiable tweets in Chicago said it helped them predict 19 to 25 types of crime. In the UK Cardiff University is investigating a possible statistical link between tweets and crime rates in parts of London although when you are dealing with these type of guys that seems easy. Its not quite minority report but we are revolutionsing how the police deal with internal transparency and enforcement.

Blowing content bubbles

So, can we use social media to influence and alter the future? The answer is yes we can. It’s actually getting Facebook into a bit of bother, as apparently they’ve been doing it for years  (but really they haven’t). The saying goes that if you say a lie loudly enough and enough times, people will believe it. Well, this is true - just look at Fox news in the US.

The same goes for opinions. If you have a strong opinion and share this with likeminded people across social media, you start off a chain reaction. You share a post, then your friend shares a similar one, and so on until your news feed is filled with posts that are voicing that opinion and none that are challenging it.

A website algorithm then selectively guesses what information you would like to see based on location, past click behaviour and search history. As a result, you become separated from information that disagrees with your viewpoints, effectively isolating yourself in a cultural or ideological bubble.

This is a filter bubble, and its effect on people (especially younger, more social media engaged users) should not be underestimated.

So how does this change the future? Well, it’s hardly the butterfly effect but it is still effective. If you create a strong message, find an applicable audience and then guide the conversation with content placed through paid social media, influencers and organic posts, you’re effectively creating this filter bubble within your target group. This bubble means that individuals are more likely to agree with your point of view and there are less opinions to challenge this. As a result, you become highly influential in a space which you can then utilise for your product.

So if you want to be able to predict the future, change it and rule the world? Well, we probably can’t do that last one, but we are awfully good at paid social media so give us a call.

It takes 20 years to build a reputation and 5 minutes to ruin it. Make sure your social media management is helping not damaging your brand by reading our post "How to boost your brand reputation with social media management"

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