How Big Data in Political Campaigns to Drive Election Success?

Big data has significantly transformed political communication by enabling campaigns to gather, analyze, and utilize vast amounts of information about citizens to inform their strategies and tactics. This allows a more nuanced understanding of voter behavior and the development of highly targeted messages.

What is Big Data in Political Communication?

Big data in political communication refers to the computational and analytic processes applied to large quantities of information about citizens compiled from multiple sources. This data is often extremely diverse and precise, going beyond traditional voter rolls to include private information collected by companies through social networks, online purchases, and browser history, revealing tastes and habits. The sheer volume and complexity of this data necessitate the use of specialized software for analysis.

Sources of Big Data for Political Campaigns

Political campaigns gather voter data from a variety of sources:

Government authorities maintain voter registration databases that contain personal data, including date of birth, gender, address, phone number, and voting history. Voting history, specifically whether a citizen votes and their voting method, is considered the most critical data for predicting voter turnout.

Social media applications like Facebook, X, and Instagram provide engagement metrics, interests, and sentiment analysis. Data can be collected through APIs (Application Programming Interfaces), which enable social media services to give researchers access to it. However, individual agreements and internal review boards might be necessary to collect more extensive datasets.

Online interactions include website visits, email responses, and surveys.

Third-party data, including consumer data (e.g., purchase history from Amazon), lifestyle data (e.g., newspaper subscriptions, car ownership), and attitudinal data, can be bought and combined with other information to create detailed profiles.

Publicly available information, such as census data.

Applications of Big Data in Political Campaigns

Campaigns utilize big data for various purposes aimed at maximizing their probability of victory:

Voter Targeting and Microtargeting: Big data enables campaigns to identify new niche demographics on voter rolls and target them with finely tuned messages designed to increase voter turnout and persuade undecided voters. This involves dividing the electorate into groups based on shared characteristics, beliefs, or behaviors, such as “environmentally conscious millennials” or “fiscal conservatives nearing retirement.”

Outreach and Communication: Data helps determine the most effective means and channels of communication to reach specific voter segments with relevant information. This includes tailoring content for direct mail, robocalls, door-to-door canvassing, and social media advertising. Campaigns can even personalize messages based on voters’ likely responsiveness to specific campaign treatments identified through randomized field experiments.

Mobilization: By identifying likely supporters, campaigns can focus their get-out-the-vote (GOTV) efforts on these individuals to ensure they turn out on election day.

Campaign Evaluation: DevaEvaluateto eas inal-timpatrack activities in real-time, used a “battleground optimizer” to run simulations and adjust campaign activities based on predicted outcomes. Analyzing data from Facebook activities can also show the impact of specific campaign actions.

Understanding Voter Sentiment: Real-time sentiment analysis tools track reactions to campaign messaging on social media, enabling timely adjustments to ensure communications remain relevant.

Identifying Trends: Big data can reveal hidden patterns in voter behavior and preferences that may not be apparent through traditional methods, such as polling. For example, analyzing online gaming activity could reveal unexpected demographic insights.

Examples of Big Data Usage

Barack Obama’s campaigns in 2008 and 2012 are often cited as pivotal moments in the adoption of big data. His 2012 campaign built a database exceeding 50 terabytes by integrating digital, field, and financial sources. They cross-referenced their data with commercial, ethnic, and religious information to identify undecided voters and opposition supporters, tailoring their outreach accordingly.

In India’s 2014 General Election, the BJP heavily relied on big data and campaign managers to develop effective political strategies, resulting in a sweeping victory that paved the way for political marketing in India.

Donald Trump and Hillary Clinton utilized big data techniques in the 2016 US presidential election. The Trump campaign reportedly aimed to target voters using psychological profiling based on personality traits.

In South Korea, Professor Hannah Park’s research analyzed Facebook big data to understand the dynamics of the presidential election and the impeachment process, tracking user comments and movements across different candidates’ pages.

Benefits of Big Data in Political Communication

• Increased Efficiency: Campaigns can concentrate their resources where they will be most effective, leading to a better return on investment.
• Improved Targeting: Messages can be tailored to resonate with specific voter segments, potentially increasing their impact.
• Enhanced Voter Turnout: By identifying and targeting likely supporters, campaigns can enhance their Get Out the Vote (GOTV) efforts.
• Real-time Adaptability: Monitoring data enables campaigns to adjust their strategies and messaging promptly in response to shifting voter sentiment.
• Greater Transparency: Big data can make it more challenging to conceal issues such as political corruption and inadequate service.

Challenges and Ethical Considerations

• Privacy Concerns: The detailed level of voter targeting raises serious concerns about the invasion of privacy and the potential for misuse of personal data. The Cambridge Analytica scandal highlighted the ethical issues surrounding the collection and use of Facebook data without explicit consent for political advertising purposes.
• Data Security and Theft: Collected data is vulnerable to online attacks and data theft, which could be used for negative purposes.
• Potential for Discrimination and Stigmatization: Big data could discriminate against or stigmatize certain user groups.
• Manipulation and Misinformation: Data can spread misinformation, create echo chambers, and manipulate public opinion by reinforcing existing beliefs and siloing information.
• Maintaining Data Legitimacy: Verifying the authenticity of data, especially from social media, and identifying and removing bots and trolls are significant challenges.
• Data Protection Laws: Different countries have laws that limit the collection and use of personal data in political campaigns. Europe’s GDPR, for example, provides a stringent framework. These issues are more pronounced in countries with weaker data protection.
• Unequal Access to Resources: The cost of collecting and analyzing big data can create an uneven playing field, where well-funded campaigns have a significant advantage.

Future Trends

• Enhanced Individual Targeting: The trend is towards using individual data, enhanced with psychological and behavioral indicators, to deliver tailor-made messages to each voter. However, some experts are concerned that this can weaken public discourse and democratic processes by creating information silos.
• Increased Automation and AI: Artificial intelligence and machine learning will play a greater role in processing data, making inferences, and automating campaign strategies.
• Integration of Online and Offline Data: Campaigns will continue integrating digital and offline data to create a more comprehensive understanding of voters.
• Emphasis on Data Science and Analytics: The role of data scientists and analysts within political campaigns will continue to grow as campaigns increasingly rely on data-driven decision-making.

Key Uses of Big Data in Political Campaigns

Voter Targeting and Microtargeting: This is a central application. It allows campaigns to identify new niche demographics on voter rolls and target them with finely tuned messages designed to increase turnout and persuade undecided voters. Campaigns can segment the electorate based on shared characteristics, such as issue preferences, demographics (including income, age, ethnicity, education, marital status, party affiliation, and independent voter status), geographic location, and online behavior and interests. The BJP’s 2014 victory in India relied heavily on sophisticated campaign management to develop effective political strategies.

Outreach and Communication: Big Data informs the selection of the most effective communication channels and tailoring message content for different voter segments. This includes personalizing outreach through direct mail, robocalls, door-to-door canvassing, and social media advertising. Even the timing of campaign activities can be optimized based on data-driven simulations, as reportedly done by the Trump campaign with a “battleground optimizer.”

Voter Mobilization (GOTV): By identifying likely supporters through data analysis, campaigns can target their get-out-the-vote efforts specifically to these individuals, ensuring they cast their ballots.

Campaign Evaluation and Adjustment: Assess campaign activities in real-time, allowing for adjustments to strategy and messaging. Monitoring social media sentiment provides immediate feedback on how messages are being received and understood. A state election campaign reportedly utilized Facebook data to measure the “lift” from specific campaign activities.

Understanding Voter Sentiment and Identifying Trends: Analyzing Big Data can uncover hidden patterns in voter behavior and preferences that traditional polling might miss. For example, analyzing online gaming activity could reveal unexpected demographic insights. Professor Hannah Park analyzed Facebook data in South Korea to understand the dynamics of presidential elections and the impeachment process.

Analytical Techniques

Campaigns employ various analytical techniques to process Big Data and generate actionable insights:

• Predictive Analytics: Models are developed to predict individual-level probabilities of voters engaging in specific political behaviors, supporting particular candidates, and responding to targeted campaign interventions. Key outputs include behavioral scores (predicting traits such as voting or donating) and responsiveness scores (predicting reactions to campaign treatments).
• Machine Learning and AI: Automation and artificial intelligence are increasingly used for rapid data processing, inference generation, and automated campaign strategies.
• Sentiment Analysis: Tools monitor social media to gauge real-time public reaction to campaign messaging.
• A/B Testing: Experiments are conducted to evaluate the effectiveness of different campaign messages and tactics.
• Network Analysis: Used to understand connections and influence within social media networks, particularly in identifying propaganda networks.

Benefits and Challenges

The use of Big Data offers several potential benefits:

• Increased Efficiency: Resources can be concentrated on the most effective outreach methods and target audiences, allowing for a more focused approach.
• Improved Targeting: Messages can be tailored for greater resonance with specific voter segments.
• Enhanced Voter Turnout: GOTV efforts can be strategically directed towards likely supporters.
• Real-time Adaptability: Campaigns can quickly adjust strategies based on data feedback.
• Potential for Transparency: Big Data can help uncover issues like corruption.

Significant challenges and ethical considerations

• Data Security and Theft: Collected data is vulnerable to breaches.
• Potential for Discrimination and Stigmatization: The data could be used to target specific groups in a negative manner.
• Maintaining Data Legitimacy: Identifying and removing bots and fake accounts is crucial.
• Data Protection Laws: Varying regulations across countries impact campaign data usage, such as the GDPR in Europe.
• Unequal Access to Resources: The cost of Big Data analytics can create an advantage for well-funded campaigns.

Social Media as a Source of Big Data

• Social media channels like Facebook, X, and Instagram provide campaigns with vast amounts of unstructured data, including engagement metrics, interests, and sentiment analysis.
• The data generated on these platforms reveals individuals’ tastes and habits. This can range from Facebook friends and likes to YouTube views, LinkedIn profiles, Pinterest activity, Tumblr, Instagram, Reddit, and who a person follows or retweets on Twitter.
• Professor Hannah Park discusses analyzing Facebook’s big data in the context of South Korean presidential elections, tracking user comments and movements across candidates’ pages. She downloaded data about impeachment from Facebook Live streams and identified commenters, tracking their movement to different candidate pages.
• Rappler investigated the social media machinery used during the Philippine presidential elections. They identified fake accounts and propaganda networks by analyzing both unstructured and structured data from platforms such as Facebook and Twitter. They tracked the spread of articles and identified networks of counterfeit accounts influencing public opinion.
• Campaigns can collect social media data through APIs (Application Programming Interfaces), which allow researchers access to the data. User content often becomes site content. Alternative methods involve individual agreements and internal review boards for more extensive datasets.

Applications of Social Media Big Data in Political Communication

Microtargeting: Social media data and other sources allow campaigns to identify new niche demographics and target them with finely tuned messages on social media platforms. This helps increase voter turnout and persuade undecided voters.

Outreach and Communication: Data helps determine the most effective social media channels and content to reach specific voter segments with relevant information. Campaigns can personalize social media advertisements based on a voter’s likely responsiveness to particular messages.

Understanding Voter Sentiment: Real-time sentiment analysis tools can track reactions to campaign messaging on social media, enabling timely adjustments. Professor Park used the number of responses (likes, shares, comments) on Facebook Live broadcasts to gauge public sentiment during the South Korean impeachment process.

Identifying Trends: Analyzing social media activity can uncover hidden voter behavior and preference patterns.

Combating Disinformation: Organizations like Rappler utilize big data from social media to investigate and expose propaganda networks and fake accounts that spread disinformation during elections. They analyzed the spread of fake news articles and identified the Facebook pages and networks involved.

Tools and Technologies

• Specialized software is necessary to analyze the sheer volume and complexity of social media big data.
• Sentiment analysis tools are used to monitor reactions to campaign messaging.
• Network analysis techniques are employed to understand the connections and spread of information on social media.

Examples of Social Media Big Data in Campaigns

• Obama’s campaigns utilized social media and integrated it with other data sources to target voters. His 2012 campaign established an extensive database that incorporated digital sources.
• The Trump campaign also employed big data techniques, potentially including psychological profiling based on personality traits derived in part from online activity.

Impact of Big Data on Political Campaigns

• Big data has revolutionized politics, making past campaign strategies seem like “ancient history.”
• It has shifted from broad messaging through traditional media, such as radio and TV, towards highly targeted, data-driven strategies.
• Predictive scores have increased dramatically since 2004, potentially yielding sizable gains for campaigns that harness them effectively.
• It has amplified the importance of traditional campaign work, making grassroots tactics more efficient and cost-competitive.

Key Tools and Technologies

Modern campaigns utilize various technologies to manage and analyze big data:

• Data Management Platforms (DMPs) & CRM Systems organize and segment voter data by integrating various sources.
• Artificial Intelligence & Machine Learning: To process massive datasets, uncover patterns, predict voter behavior, and optimize outreach more accurately. Automation enables quicker inferences and the creation of distinct voter pools.

Privacy Concerns and Potential for Misuse

The detailed level of voter targeting enabled by big data raises serious privacy concerns. It can feel like invasive surveillance of voters’ preferences and behaviors.

• Lack of voter consent: Voters often have no say in what information is collected about them.
• Potential for discrimination and stigmatization: The use of big data may lead to these adverse outcomes.
• Tracking and destabilization: Concerns exist that those elected could use collected data to track and potentially destabilize their opponents’ supporters.
• Online attacks and data theft: The vast amounts of data collected are vulnerable to online attacks and data theft, which could be used for negative purposes.

The Cambridge Analytica Scandal

The Cambridge Analytica scandal is a well-known example of the misuse of personal data in political campaigns. The firm accessed personal data from millions of Facebook users without explicit consent through a third-party app. This data harvesting allowed them to build detailed voter profiles and microtarget individuals with highly personalized political ads. This event highlighted significant ethical concerns regarding privacy and data protection, leading to widespread criticism and regulatory investigations.

Data Protection Laws and Regulations

The sources indicate varying levels of data protection laws across different regions:

Europe and Germany: These regions have stringent data protection laws, which prohibit the use of individual-level voter information in the same manner as in the US. In Germany, while clustering is possible, individual-level data on party affiliation is not legally available for campaigns. The GDPR in the European Union provides a stringent framework for handling voter data in a transparent and privacy-respecting manner.

United States: While there is a collection of detailed individual voter data, the data is often considered opt-in as it’s information shared intentionally in some way. However, privacy concerns still exist.

Perspectives on Data Privacy

Panelists in the YouTube video “#ACPC17 – Big Data in Political Communication” express various views:

• Some believe data can help the democratic process by uncovering hidden patterns and increasing transparency.
• Others emphasize the need for careful consideration of the downfalls and the importance of individual choices regarding data scrutiny.
• Concerns are raised about the potential for existing power structures, such as governments and tech companies, to utilize big data for their purposes.
• The importance of educating citizens about the implications of big data is highlighted.

The Promise of Big Data in Political Communication

Enhanced Voter Targeting and Microtargeting: Big data enables campaigns to identify niche demographics on voter rolls and tailor messages to them with precision. This is achieved by analyzing vast information about citizens compiled from multiple sources, including voter registration databases, social media, online interactions, and commercial data. This enables campaigns to go “small” and communicate tailored messages to specific segments.

Predictive Analytics and Behavioral Insights: Data science provides tools for predictive analytics, allowing campaigns to understand and influence voter behavior. By analyzing behavioral data, such as voting history, issue preferences, and demographic and geographic information, campaigns can create models to predict voter turnout and candidate preferences.

Improved Campaign Efficiency and Resource Allocation: Targeting voters more precisely allows campaigns to concentrate their resources where they will be most effective. By identifying undecided voters and opposition supporters, campaigns can prioritize key areas for outreach, such as door-to-door canvassing, direct mail, phone calls, and social media outreach.

Real-time Campaign Adjustment: As Professor Avoid mentioned regarding the Trump campaign, some campaigns utilize data to run simulations and adjust their campaign activities in real time based on predicted outcomes of events and issues. This allows for a more dynamic and data-driven approach to campaign strategy.

Understanding Public Sentiment and Trends: Analyzing large datasets from social media and online interactions can reveal public sentiment, track the spread of information (including misinformation), and identify emerging trends. Rappler’s mood meter, a tool-like feature, aims to capture the electorate’s feelings in real-time. Professor Park’s research in South Korea demonstrated how Facebook data could be used to track public reaction to political events.

Identifying Hidden Patterns: Big data can uncover hidden patterns in voter behavior and preferences that might not be apparent through traditional polling or surveys. This can lead to new insights into what motivates voters and how to effectively reach them.

The Hype and Limitations of Big Data in Political Communication

Not a Radical Transformation: Although impactful, the analytics revolution has not led to a radical transformation of campaigns, such as television. Professor Gimpel suggests that much of what is considered “big data analytics” utilizes tools and techniques that are not entirely new.

Marginal Effects: Professor Gimpel believes that while big data can have a marginal influence and make a difference in close races, it is not the sole determinant of electoral outcomes. Overstating its impact is common in journalistic accounts.

Data Quality and Accuracy: The accuracy and reliability of big data are crucial but can be challenging to achieve. Self-reported data can be subject to exaggeration or dishonesty. Additionally, data sets may be incomplete or contain outdated information. Professor Gimpel distinguishes between “big conventional data” (structured data) and “big unstructured data” (web-based data), noting that unstructured data requires significant pre-processing and cleaning.

Correlation vs. Causation: While predictive scores can identify correlations between voter characteristics and behavior, they do not necessarily establish causation. Just because a certain demographic is more responsive to a message does not mean that demographic characteristic is the cause of that responsiveness.

The Importance of Traditional Campaign Work: Data analytics has arguably underscored the significance of traditional campaign work, including message polling and grassroots organizing. Data can inform these activities, but it does not replace the need for effective communication and persuasion.

Data Overload and the Need for Domain Knowledge: Campaigns can be overwhelmed by the sheer volume of data, and having more data is not always an improvement. Professor Gimpel emphasizes the crucial need for “domain knowledge” and the ability to interpret findings within their political and social context. Understanding the underlying factors driving voter behavior is essential for effectively utilizing data insights.

Ethical Concerns and Privacy Risks: Concerns exist regarding the lack of voter consent, potential for discrimination, and the creation of echo chambers through hyper-personalized messaging.

Uneven Adoption and Capabilities: Not all campaigns or political actors have the same level of access to or expertise in utilizing big data.

The Static Nature of Some Data: Professor Gimpel points out that while efforts are being made to track voter movement, some data analyzed can be static, while the population is more flexible. This can limit the accuracy of predictions over time.

The Role of Big Data in Identifying Voters for GOTV

• Political campaigns now utilize data analytics to identify individuals who are already likely to support their candidate or cause but may require additional encouragement to vote. This involves analyzing vast amounts of information from various sources, including voter registration databases, social media, online interactions, and commercial data.
• Companies like L2 Political aggregate over 180 million voter records, including publicly shared information and voting history, allowing campaigns to gain a detailed understanding of the electorate for GOTV purposes.
• Predictive analytics, which leverages historical voting patterns and demographic information, enables campaigns to develop models that accurately predict voter turnout. This allows them to identify “likely supporters who may require additional encouragement to vote.”
• The Environmental Voter Project is a specific example. It uses data analytics to identify environmentalists who do not consistently vote and then applies behavioral science to mobilize them.

The Application of Behavioral Science for GOTV Messaging

Modern campaigns are moving away from the traditional assumption that the decision to vote is solely a logical, rational choice based on cost-benefit analysis. Instead, they are increasingly leveraging insights from behavioral science.

Peer pressure and social norms have proven powerful motivators for voter turnout. Messages emphasizing that “everybody’s voting” can be more effective than those highlighting the importance of an individual’s vote in a close election.

Framing voting as an expression of self-identity (“I am a voter”) has also dramatically increased turnout. Campaigns send reminders and information about polling locations to infrequent voters identified through data.

The Environmental Voter Project significantly boosted turnout by sending letters to inform individuals that their neighbors were voting and providing their public voting records (without revealing who they had voted for). Similar tactics were reportedly used by the Ted Cruz, Bernie Sanders, and Hillary Clinton campaigns during the Iowa caucus.

Campaigns are conducting A/B testing of different messages to optimize engagement and identify what resonates best with specific voter segments for Get Out the Vote (GOTV) efforts.

The Synergy of Big Data and Behavioral Science in GOTV

• Big data enables campaigns to target specific individuals who have been identified as needing GOTV intervention. Instead of broad demographic outreach, campaigns can send tailored, behaviorally informed messages to these individuals.
• Real-time campaign adjustments can be made based on data and the observed effectiveness of different GOTV messages.
• While the ability to precisely target voters with tailored messages raises ethical concerns about privacy and potential manipulation, the combination of big data and behavioral science represents a significant shift in how campaigns approach voter mobilization. Campaigns aim to maximize their impact with limited resources by focusing on high-probability voter segments identified through data and motivated by behaviorally sound messaging.

Data Protection and Privacy Regulations

Several sources emphasize the crucial role of government in establishing and enforcing data protection and privacy regulations to address the ethical concerns arising from the use of big data in political campaigns.

Professor Avoid notes that in Germany and Europe, severe data protection laws create a significant contrast with the US, limiting campaigns’ ability to use individual-level data. In Germany, campaigns can primarily use geographical targeting based on historical election results, socioeconomic data, and polling; however, individual party affiliations are not legally accessible.

In France and Europe, data protection laws theoretically require citizen consent for the use of personal information; however, these rules are often softened or bypassed in practice.

The European Union’s GDPR is mentioned as a stringent framework that political campaigns must navigate, ensuring transparent data handling and respect for privacy.

The Cambridge Analytica scandal is a prime example highlighting significant ethical concerns regarding privacy and data protection, leading to regulatory investigations and broader discussions about data analytics in elections.

Government’s Role in Harnessing and Managing Big Data

Maria Ressa suggests that governments should proactively harness big data to jumpstart development in developing countries. She envisions data-driven decisions that are not solely driven by politics, emphasizing the potential for technology to aid in resource allocation and address public needs.

Professor Park mentions the open data movement, in which governments are beginning to release administrative data to stimulate the economy and promote transparency. He also notes a trend in which citizens demand governments open their data, allowing private companies and individuals to leverage this information for economic growth and social democracy.

Professor Avoid discusses the debate in Germany regarding publicly available data, such as smart meter information in the energy sector, raising questions about who can access and utilize this data. He also mentions Estonia’s approach of providing citizens with a personal identity card to control their data sharing across various services, suggesting a potential future direction.

Balancing Data Utility with Privacy Rights

• The panelists in the YouTube discussion acknowledge the inherent tension between the benefits of using big data and the need to protect individual privacy rights.
• Professor Avoid believes that, ultimately, it might come down to individuals opting into the level of data scrutiny they are comfortable with.
• Transparency, consent, and strict adherence to data protection laws are crucial for maintaining the integrity of the democratic process and fostering public trust in political campaigning.

Conclusion

Big data altered the landscape of political communication, offering powerful tools for campaigns to understand and engage with voters. While these technologies present significant opportunities for more efficient and targeted outreach, they also raise critical ethical concerns regarding privacy, manipulation, and the potential for deepening political divisions. Navigating these challenges through robust regulatory frameworks, ethical considerations, and public education will ensure that big data enhances rather than undermines the democratic process.