Data Mining for Predictive Social Network Analysis – Brazil Elections Case Study

Here are the techniques used for a proof-of-concept that effectively analyzed Twitter Trend Topics to predict regional voting patterns in the 2014 Brazilian presidential election.

By Elder Santos (TOPTAL).

Social networks, in one form or another, have existed since people first began to interact. Indeed, put two or more people together and you have the foundation of a social network. It is therefore no surprise that, in today’s Internet-everywhere world, online social networks have become entirely ubiquitous.

Within this world of online social networks, a particularly fascinating phenomenon of the past decade has been the explosive growth of Twitter, often described as “the SMS of the Internet”. Launched in 2006, Twitter rapidly gained global popularity and has become one of the ten most visited websites in the world. As of May 2015, Twitter boasts 302 million active users who are collectively producing 500 million Tweets per day. And these numbers are continually growing.

Given this enormous volume of social media data, analysts have come to recognize Twitter as a virtual treasure trove of information for data mining, social network analysis, and information for sensing public opinion trends and groundswells of support for (or opposition to) various political and social initiatives. Twitter Trend Topics in particular are becoming increasingly recognized as a valuable proxy for measuring public opinion.

social network analysis and data mining

This article describes the techniques I employed for a proof-of-concept that effectively analyzed Twitter Trend Topics to predict, as a sample test case, regional voting patterns in the 2014 Brazilian presidential election.

The Election

General presidential elections were held in Brazil on October 5, 2014. No candidate received more than 50% of the vote, so a second runoff election was held on October 26th.

In the first round, Dilma Rousseff (Partido dos Trabalhadores) won 41.6% of the vote, ahead of Aécio Neves (Partido da Social Democracia Brasileira) with 33.6%, and Marina Silva (Partido Socialista Brasileiro) with 21.3%. Rousseff and Neves contested the runoff on October 26th with Rousseff being re-elected by a narrow margin, 51.6% to Neves’ 48.4%. The analysis in this article relates specifically to the October 26th runoff election.

Partido dos Trabalhadores (PT) is one of the biggest political parties in Brazil. It is the political party for the current and former presidents, Dilma Roussef and Luis Inacio Lula da Silva. Partido da Social Democracia Brasileira (PSDB) is the political party of the prior president Fernando Henrique Cardoso.

Data Mining and Extracting Twitter Trend Topic Data

I began social media data mining by extracting Twitter Trend Topic data for the 14 Brazilian cities for which data is supplied via the Twitter API, namely: Brasília, Belém, Belo Horizonte, Curitiba, Porto Alegre, Recife, Rio de Janeiro, Salvador, São Paulo, Campinas, Fortaleza, Goiânia, Manaus, and São Luis.

I queried the Twitter REST API to get the top 10 Twitter Trend Topics for these 14 cities in a 20 minute interval (limited by some restrictions that Twitter has on its API). Limiting the query to these 14 cities is done by specifying their Yahoo! GeoPlanet WOEIDs (Where On Earth IDs).

For this proof-of-concept, I used Python and a Twitter library (cleverly called “twitter”) to get all the social network data for the day of the runoff election (Oct 26th), as well as the two days prior (Oct 24th and 25th). For each day, I performed about 70 different queries to help identify the instant trend topics.

Below is an example of the JSON object returned in response to each query (this example was based on a query for data on October 26th at 12:40:00 AM, and only shows the data for Belo Horizonte).

Brief Intro to Social Network Analysis

Social Network Theory is the study of how people, organizations, or groups interact with others inside their network. There are three primary types of social networks:

  • Egocentric networks are connected with a single node or individual (e.g., you and all your friends and relatives).
  • Socio-centric networks are closed networks by default. Two commonly-used examples of this type of network are children in a classroom or workers inside an organization.
  • Open system networks are networks where the boundary lines are not clearly defined, which makes this type of network typically the most difficult to study. The type of socio-political network we are analyzing in this article is an example of an open system network.

Social networks are considered complex networks, since they display non-trivial topological features, with patterns of connection between their elements that are neither purely regular nor purely random.

Social network analysis examines the structure of relationships between social entities. These entities are often people, but may also be social groups, political organizations, financial networks, residents of a community, citizens of a country, and so on. The empirical study of networks has played a central role in social science, and many of the mathematical and statistical tools used for studying networks were first developed in sociology.

Establishing the Network

To create a network using the Twitter Trend Topics, I defined the following rules:

  • Each city is a vertex (i.e., node) in the network.
  • If there is at least one common trend topic between two cities, there is an edge (i.e., link) between those cities.
  • Each edge is weighted according to the number of trend topics in common between those two cities (i.e., the more trend topics two cities have in common, the heavier the weight that is attributed to the link between them).

For example, on October 26th, the cities of Fortaleza and Campinas had 11 trend topics in common, so the network for that day includes an edge between Fortaleza and Campinas with a weight of 11:

In addition, to aid the process of weighting the relationships between the cities, I also considered topics that were not related to the election itself (the premise being that cities that share other common priorities and interests may be more inclined to share the same political leanings).

Although the order of the trend topics could potentially have some significance to the analysis, for purposes of simplification of the proof-of-concept, I chose to ignore the ordering of the topics in the trend topic list.