[DSC Europe 23] Alen Kisic - How can do Facebook data and machine learning algorithms predict political elections?
Download as PPTX, PDF0 likes11 views
The document investigates how Facebook data and machine learning algorithms can predict political election outcomes, focusing on the development of predictive models using social media interactions. It highlights the inadequacies of previous research, outlines the methodology for analysis using data from French local elections, and demonstrates that Facebook activity, particularly the total number of page likes, is a significant predictor of election results. The study concludes with recommendations for political campaigns on social media and acknowledges limitations in its scope.
[DSC Europe 23] Alen Kisic - How can do Facebook data and machine learning algorithms predict political elections?
- 1. How can Facebook data and machine learning algorithms predict political elections?
- 2. Introduction ◉ Political elections are the daily routine of every democratic society and most European countries have an average of one election every year (local, parliamentary, presidential, EU parliament ) . ◉ The eternal question: predicting the results. ○ everyone is interested: citizens, political actors who, create strategies and activities . 2
- 3. Introduction ◉ Development of social networks: powerful tool for communication with voters! ○ Can we develop predictive model based on candidates activity on the social network Facebook that is comparable to traditional models based on polls? 3
- 4. Related work ◉ Review of the literature identified lack of previous research: ◉ a model for measuring the effectiveness of political campaigns on social networks has not been developed: variables that are suitable for evaluation in one specific domain are not suitable for evaluation in another domain, ◉ in most research, data was collected only on Twitter , ◉ most of the previous works do not have a component of predictive analytics, ◉ no models have been developed that would determine to what extent activity on the social network Facebook contributes to predicting the outcome of the election. 4
- 5. Related work ◉ Review of the literature identified lack of previous research: ◉ Various models are based on the number of friends or followers on social networks: the conclusions are generalized based on too few variables that do not include the interaction between the candidate and the potential voter. ◉ Most models are based on sentiment analysis - not sufficient to determine the impact on the outcome of the election. ◉ Current methods are not sufficient - necessity of machine learning. ◉ Many models are not empirically confirmed. 5
- 6. Research objectives ◉ To determine the predictive power of models based on the social network Facebook and compare it with other types of research . ◉ To determine which variables are the most significant predictors of the outcome of the election . ◉ To determine the significance of the temporal data component of the Facebook social network . ◉ To argue which of the four machine learning methods provides the most accurate predictive models of local election outcomes. 6
- 7. Research flow Predictive models development based on Facebook data and four machine learning algorithms Data collection and preparation: Facebook data Model evaluation Comparison of models with opinion polls data 7
- 8. Research methodology CRISP DM 8 EVALUATION DEPLOYMENT PROBLEM UNDERSTANDING DATA PREPARATION DATA UNDERSTANDING MODELLING
- 9. Research methodology – CRISP DM 9 EVALUATION DEPLOYMENT PROBLEM UNDERSTANDING DATA PREPARATION DATA UNDERSTANDING MODELLING Research goals: - determine the predictive power of election outcome models based on the social network Facebook and compare it with other types of research, - determine which variables are the most significant predictors of the outcome of the election.
- 10. Research methodology – CRISP DM 10 EVALUATION DEPLOYMENT PROBLEM UNDERSTANDING DATA PREPARATION DATA UNDERSTANDING MODELLING France local elections: all cities with more than 100 000 habitants (41 city), 225 candidates – examination and downloading data from their Facebook pages Number of events, photos, links, videos, statuses – their comments, shares and likes.
- 11. Research methodology – CRISP DM 11 EVALUATION DEPLOYMENT PROBLEM UNDERSTANDING DATA PREPARATION DATA UNDERSTANDING MODELLING 25 variables included: - 24 input variables: the candidate's activity and the reactions of the page followers to the candidate's activity, two variables related to the gender of the candidate and the affiliation of the candidate to a political party. - one output variable: the result in the elections measured by the percentage of votes of the candidates in the elections.
- 12. Research methodology – CRISP DM 12 EVALUATION DEPLOYMENT PROBLEM UNDERSTANDING DATA PREPARATION DATA UNDERSTANDING MODELLING Why Facebook? Chen and Chang (2017), claim that political campaigns are increasingly "fleeing" to Facebook - social network with the largest number of users. Facebook is the most used source of political news for Millennials and Generation X (ages 18 to 51) (Pew Research Center, 2015). France - 70% of the population of those who use social networks is on the Facebook (Chaffey, 2019). Singh and colleagues (2020), who conducted the research only on Twitter, stated the need to include other social networks.
- 13. Research methodology – CRISP DM 13 EVALUATION DEPLOYMENT PROBLEM UNDERSTANDING DATA PREPARATION DATA UNDERSTANDING MODELLING Attributes taken into account are: city and turnout in the city, party, total number of page likes, number of photos, number of statuses (text), number of links, number of videos, number of events created, number of photo shares, number of status shares, number of link shares, number of video shares, number of likes photo, number of status likes, number of link likes, number of video likes, number of photo comments, number of status comments, number of link comments, number of video comments, publication time. Transformation into relative sizes with respect to the number of voters.
- 14. Research methodology – CRISP DM 14 EVALUATION DEPLOYMENT PROBLEM UNDERSTANDING DATA PREPARATION DATA UNDERSTANDING MODELLING Data description through descriptive statistics . Most variables have an exponential distribution of values: - characterized by a high probability of occurrence of smaller values, and a low probability of occurrence of large values. - values of the arithmetic mean are higher than the values of the median.
- 15. Research methodology – CRISP DM 15 EVALUATION DEPLOYMENT PROBLEM UNDERSTANDING DATA PREPARATION DATA UNDERSTANDING MODELLING Correlation analysis - the highest correlation between the number of event sharing and number of event likes , - correlation is positive, which indicates that with the increase in event likes, the number of event shares also increases, and vice versa . - variable Result - has the highest linear relationship with the variable Total number of page likes
- 16. Research methodology – CRISP DM 16 EVALUATION DEPLOYMENT PROBLEM UNDERSTANDING DATA PREPARATION DATA UNDERSTANDING MODELLING Outliers - ML algorithms are sensitive to extreme values. - Interquartiles were used to identify outliers. - Missing values - Imputation was performed by inserting data instead of missing values . - missing values were replaced by the mean values of the variables.
- 17. Research methodology – CRISP DM 17 EVALUATION DEPLOYMENT PROBLEM UNDERSTANDING DATA PREPARATION DATA UNDERSTANDING MODELLING Data normalization - data were normalized with respect to the number of voters in each city. The original value of each variable is divided by the number of voters in the city to which it refers. - Min- max normalization - all variables to a scale from 0 to 1.
- 18. Research methodology – CRISP DM 18 EVALUATION DEPLOYMENT PROBLEM UNDERSTANDING DATA PREPARATION DATA UNDERSTANDING MODELLING - Predictive models were developed using four machine learning algorithms: - optimization of hyperparameters was carried out of each individual algorithm in order to prevent overtraining of the model, and to obtain high-quality, reliable and accurate predictive models.
- 19. Research methodology – CRISP DM 19 EVALUATION DEPLOYMENT PROBLEM UNDERSTANDING DATA PREPARATION DATA UNDERSTANDING MODELLING Four types of machine learning algorithms - Machine learning based on information (Classification and regression trees). - Machine learning based on similarity (k-nearest neighbors), - Machine learning based on probability (Bayesian networks), - Error-based machine learning (artificial neural networks).
- 20. Models comparison Model Error K-NN 8.9889 Neural network 5.9779712 Decision tree 6.3441513 Naïve Bayes 12.4532 20
- 21. Models comparison Model Reliability K-NN 0.5044 Neural network 0.8093 Decision tree 0.753 Naïve Bayes -0.243 21
- 22. Model comparison ◉ In order to compare the predictive models obtained by machine learning algorithms and the results of pre-election polls, data provided by the French Institute for Public Opinion Research, IFOP, was used. ◉ The data refer to 6 cities: Paris, Lyon , Marseille , Rennes , Nantes and Bordeaux , thus including data for a total of 44 candidates in the elections. ◉ There is difference in error of predictive models obtained by machine learning algorithms and polling! 22
- 23. Research methodology – CRISP DM 23 EVALUATION DEPLOYMENT PROBLEM UNDERSTANDING DATA PREPARATION DATA UNDERSTANDING MODELLING Determination of the most significant predictors of election outcomes based on the obtained predictive models. Sensitivity analysis of each predictive model was performed.
- 24. Prediction of results N kNN rank ANN rank Bayes rank Decision tree rank Average rank Overall page likes 1 1 1 1 1 Number of events 6 7 3 4 1 Number of photos 5 8 5 9 6 Number of links 3 9 7 7 6.5 Number of statuses 4 3 9 3 4.75 24
- 25. Prediction of results N kNN rank ANN rank Bayes rank Decision tree rank Average rank Number of photo likes 8 6 8 6 7 Number of link likes 2 2 2 2 2 Number of status likes 7 5 6 10 7 Gender 9 10 10 8 9.25 Political party 10 4 4 5 5.75 25
- 26. Results discussion ◉ Stability and consistency of the models. ◉ The total number of likes on the candidate's page is the most significant predictor in all models. ◉ The number of link likes is the second most significant predictor also in all four models. ◉ The number of statuses is the third strongest predictor of election results in two of the four observed models. 26
- 27. Results discussion ◉ The best quality models are obtained by applying the artificial neural network algorithm. ◉ Explanation lies in the data types : dummy variables are mostly numeric continuous attributes . ◉ The models with the lowest reliability and accuracy values were obtained by the Naive Bayesian classifier ◉ Naive Bayesian classifier works with categorical variables, so it is necessary to transform the continuous output into a categorical one . 27
- 28. Results discussion ◉ Social media as an indicator for predicting election outcomes ◉ the absolute number of Facebook followers is a very good predictor of election outcomes ◉ the content that the candidates place via social media, as well as the reactions to the specific content that they share, is to a certain extent an indicator of the outcome of the election ◉ The results of the research lead to a better understanding of the way in which social networks present the views of voters, and indicate how the electorate can be influenced through social networks. 28
- 29. Guidelines ◉ How to run campaign on Facebook? ◉ Achieving visibility on Facebook - to get a large number of page likes or followers (various methods of increasing visibility), which generates a high place among average Facebook users in the news feed and there is a greater transmission of messages and an indirect influence on the voters. ◉ To present content from ordinary life that gives greater visibility than classic political messages that are transfer through other communication channels 29
- 30. Research limitations ◉ Only one country, France, and one elections were included. ◉ Each country has its own specificities that should be taken into account when conducting research. ◉ The opinion of the user base of one social network, Facebook, is not representative of the entire population, but it contributes to the phenomenon of explaining the results. ◉ The data refer to a very specific time, the beginning of March 2020 - the beginning of the spread of the COVID-19 virus and the lockdown . ◉ Four machine learning algorithms were used - number of algorithms left out. 30
- 31. Research contrubtions ◉ knowledge about models based on data from public opinion polls and social networks is systematized ◉ predictive models of election outcomes based on social network data were developed and evaluated and compared with data obtained from surveys ◉ the predictive power of the variables of the social network Facebook was established ◉ guidelines for using machine learning algorithms on social media data are developed. 31
- 32. “ "The whole art of politics consists in the rational management of human irrationalities." Karl Paul Reinhold Niebuhr 32
- 33. Any questions ? You can find me at ◉ kisic.alen@gmail.com Thanks! 33