With digital personalised targeting using machine-based learning posing big implications for politics and elections of the future, Morne Bekker, South African country manager and district manager for the SADC region at NetApp takes a look at how data miners are able to influence a voter’s behaviour

With the recent Zimbabwean election and well ahead of the South African ballot, many are looking towards data-driven insights to get into the voters’ head.

As with all major elections, experts and consultants scrutinise various campaigns, analysing vast quantities of information and data to determine possible trends in the voting processes and the outcomes.

Moreover, data analytics and in particular that of political information has progressed into true predictive data science, capable of offering insights using massive amounts of data that incorporates complex models and advanced algorithms to determine possible electoral outcomes.


The rise of the social activist

Modern elections however have to account for even more data than before, especially with the implementation of social media as a marketing tool to garner additional public votes, as well as a means for the public to voice their love for a particular candidate.

According to a report by Forbes, recent American Presidential elections, including the 2016 elections, bared the results on the full effects of how social media can play a major role in that of an election.

The likes of Twitter unveiled its Twitter Political Index, a joint effort between the social network powerhouse, and social data analytics provider Topsy Labs and two national polling companies, the Mellman Group and North Star Opinion Research.

The project amounted to in excess of 400-million tweets being scoured on a daily basis, focusing on political content to see exactly how the public view the presidential candidates.


The rise of social media

While canvasing tactics such as door-to-door visits and phone calls may have been effective in the past, technology has emerged as the new medium in which to influence voter behaviour. Machine learning (ML) and artificial intelligence (AI) can mean the difference between campaign success or failure, and is said to be the present and future of the electoral process.

However, according to industry experts, the trend of using big data and social media in politics didn’t emerge until 2012, where these data files and analytics are used to target potential voters online via social media.

Today, a number of campaigns distribute tailored messages to individual voters – as well as exposing these voters to specific online advertising driven by data-derived from their searches and social media activity.

These data-derived algorithms target specific voters making use of platforms such as Facebook for example, where the data which can be segmented by region, age, gender, interests, and even the likeliness to vote for a specific candidate.

Moreover, political party registered supporters and the data of users’ “likes” and reposts on Facebook and other social media platforms, provide researchers with a personal profile relating to people`s fears, relatives, interests, aspirations, and, in some instances, with the addresses of residence.


With great power comes great responsibility

And while this tactic of mining personal data from all available avenues may provide positive results for a campaign of this nature, there is a question of the protection of person information, with numerous countries reviewing and amending privacy policies to benefit that of protecting the public.

South Africa, in particular, has initiated certain sections of the Protection of Personal Information Act (POPI), promoting the protection of personal information by public and private bodies.

This is all well and said, but until the full effects of this data mining become prohibited, the data that is available presents a wider array of uses for influencing a voter, and particularly offering the opportunity for parties to understand their electorate better.

As the data authority in a hybrid cloud world, NetApp uses artificial intelligence and machine learning to constantly analyse and provide consistent insight across your data centre, so you can monitor and manage your hybrid IT multi-vendor storage, compute, and networking infrastructure.

NetApp market expertise involves the implementation of a set of predictive technologies with artificial intelligence, powerful machine-learning capabilities, and additional deployment options.