g proposed by the different political parties better, by placing them on an ideological axis – and this, as recent studies have shown, increases the involvement and participation of those who use them. Their operation is quite simple. Through a short and simple questionnaire on the users’ political position, they help them to find their location within the political spectrum by giving them information on this location with respect to the parties’ programs. This is why these instruments are often referred to as voting guidance apps or Voting Advice Applications (VAA).
VAAs respond perfectly to the scenario described above: they are a citizen-originated initiative, based on accessible technology, aiming at improving democratic processes. They also usually rely on social networks to get citizens to know about them, through a process which, in many cases, happens outside of traditional channels and actors, such as institutions and the mainstream media.
The idea of improving the citizens’ information before elections is actually an old aspiration. For the last fifty years, political scientists have shown that voters are not well informed before going to the polls, and that their beliefs may not be consistent with their vote. Much of the academic literature on the subject has dwelt on the limits that any normal person experiences in trying to follow all the political debates and the parties’ positions on all of the issues. The formula to alleviate this predicament is to be found in every person’s cognitive bias, which in the end turns up to be the mechanism by which we come to decide our vote.
This partly explains the success of the vote guidance apps, for they reduce the information costs of citizens facing elections. Just by answering a questionnaire, users can know which party comes closer to their own position. In addition, they get detailed information on each issue, so you that they can see where they agree and where they disagree with each party. Loaded with this information, it is much simpler and faster for citizens to make a decision, and they do it on the basis of reliable information. So much so, that research indicates that voters are now growing used to following the apps’ voting recommendations, provided that they are in line with their political ideas.
Another interesting aspect of the VAAs’ emergence is the extent to which these tools help us to better understand the citizens’ electoral behaviour. We know that people who use them tend to participate more than the average, but it has also been observed that the apps have a different impact on citizens depending on their level of education - and that those who benefit the most from them are the citizens with low levels of education who, after using them, show a greater interest in the electoral processes and a greater disposition to engage in them. If this evidence is confirmed by further studies, it will be a relevant finding indeed, for political participation is closely related to educational and socioeconomic levels.
Besides, citizen behaviour at the polls tend to be consistent with the results they have found in the apps. It is worth noting that only 8 % decide to change their vote when the results they get from the apps do not match what they had expected. In other words, VAAs are very effective in reinforcing citizens’ votes, but not so much in getting them to change their vote, even when the apps place them closer to other political options.
The growth and effectiveness of these tools has led some researchers to want to know better the algorithms that decide the results they give, and even to try to validate them externally. This is not a minor issue. A research carried out in 2010 in Lithuania proved that parties in this country were able to manipulate the results of these apps to their advantage.
In any case, vote guidance apps have clearly opened up the horizons of politological research. In fact, the availability of huge amounts of data is so attractive that some observers, back in 2012, were already warning about the need to study the phenomenon mor