How can data science influence elections? – Learnxt

How can data science influence elections?

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How can data science influence elections?

Suppose you are an active user of social media platforms like Facebook. In that case, you must have come across political ads that primarily influence your political interest and outcomes on Election Day. It is not a coincidence that candidates running election campaigns use audience behaviour analysis service like PredictWise – a company that uses data science to increase the registration of voters and influence election turnout to help them reach the desired voters through data-driven targeting.
The most frequent question searched on the search engines is how Data Science influences the presidential or any other significant elections. Well, let us try to fetch the exact answer to that question
USING DATA TO target voters:  
Earlier Television and Print ads in the newspapers were only two mediums to reach out to the targeted audience. Still, if we talk about politics, nowadays they need to target the entire Digital Landscape to create and build a separate space in the minds of the targeted audience. 
To do this, they require data showing the current voting status of people and which social platforms they use, how frequently they consume the podcasts and video content, what are their political beliefs, and the type of messaging that engages them into answering the polls. 
Identifying those voters in the very first place is such a hectic task. No central database currently exists documenting all registered voters, and all those are eligible to vote. Despite each maintains its data for the registered voters, but this data will not always be reliable.
During this COVID-19 Pandemic, people mostly remained quarantine and tried to shift to the areas that cost slightly less living, resulting in migration, and with this reaching them is one hell of a job now.  
PredictionWise company came up with the perfect solution for it; you do not have to identify people who no longer live where they are registered to vote. To do this, the company partners with a nonpartisan voter registration organization whose goal is to maximize the voters’ turnout. Using the GPS from voters’ smartphones, they can quickly determine whether someone has relocated or not
They have acquired data of 180 billion GPS points over three months, so if someone’s ID suggests that they have voting rights in London, but according to GPS data they are spending more nights in Manchester.
The Economist, leading up to the big election, predicted that Vice-president Biden had a chance of winning the U.S. presidential elections by 97%. The Economist predicted a 99% chance of Hillary Clinton winning in 2016. The publication itself admits that both predictions were way off.
In 2020, the election was possibly one of the closest elections in the modern history of America. Whereas in 2016 Trump won the elections, this year, in some states, candidates won with a few thousands of votes difference
Due to the COVID-19 pandemic, many factors have affected the election results – unemployment rate, impeachment, natural disasters’ response, climate change, economy, and foreign policy, debates, presidents’ heights, people’s loyalty to their party, and plenty of other factors.
To understand the role of data science in American elections, we must first get acquainted with how the elections work there. In the US, the main elections are organized for the President, the members of the House of Representatives, and the members of the Senate. Congress is the legislative body, made by the House and Senate. Congress makes the laws. The President is the head of the Executive branch and head of the state.
Depending on its population, each state elects two senators and a certain number of representatives. By national vote, the president is elected. The winner of the national vote is not always necessarily the president. Equal to the number of representatives and senators, individuals actually vote for “electors” in each state. You don’t need to win the most votes to become president; instead, you just need to win the most states.
The political parties need votes in the right places and not the most votes to control Congress. When it comes to election fundraising and campaigning, this gives data science a prominent role in American politics.
Let’s have a look at the 2020 Presidential Election predictions based on data science tools by PredictionWise:
Next president by party:

Outcome

Market

Derived Bet fair Price

Derived Predict It Price

Bet fair Back

Bet fair Lay

Republicans

54 %

$ 0.546

$ 0.525

1.82

1.84

Democrats

46 %

$ 0.472

$ 0.490

2.16

2.22

Next president:

Outcome

Market

Bet fair Derived Price

Derived Predict It Price

Bet fair Back

Bet fair Lay

Party

Donald Trump

54 %

$ 0.541

$ 0.485

1.84

1.85

GOP

Bernie Sanders

18 %

$ 0.179

$ 0.255

5.60

5.70

DEM

Joe Biden

13 %

$ 0.128

$ 0.195

7.40

7.80

DEM

Elizabeth Warren

2 %

$ 0.021

$ 0.045

46.00

50.00

DEM

Pete Buttigieg

2 %

$ 0.017

 

55.00

60.00

DEM

Amy Klobuchar

0 %

$ 0.004

$ 0.005

160.00

340.00

DEM

Mike Pence

0 %

$ 0.004

$ 0.015

240.00

280.00

GOP

Tulsi Gabbard

0 %

$ 0.002

 

370.00

480.00

DEM

Nikki Haley

0 %

$ 0.002

$ 0.005

280.00

440.00

GOP

Kamala Harris

0 %

$ 0.001

$ 0.005

1,000.00

0.00

DEM

Beto O’Rourke

0 %

$ 0.001

$ 0.005

1,000.00

0.00

DEM

Sherrod Brown

0 %

$ 0.001

$ 0.005

1,000.00

0.00

DEM

Kirsten Gillibrand

0 %

$ 0.001

$ 0.005

1,000.00

0.00

DEM

Cory Booker

0 %

$ 0.001

$ 0.005

1,000.00

0.00

DEM

Julian Castro

0 %

$ 0.001

 

1,000.00

0.00

DEM

John Kasich

0 %

$ 0.001

$ 0.005

1,000.00

0.00

GOP

John Hickenlooper

0 %

$ 0.001

 

1,000.00

0.00

DEM

Marco Rubio

0 %

$ 0.001

 

1,000.00

0.00

GOP

Tom Cotton

0 %

$ 0.001

 

1,000.00

0.00

GOP

Howard Schultz

0 %

$ 0.001

 

1,000.00

0.00

IND

Paul Ryan

0 %

$ 0.001

$ 0.005

1,000.00

0.00

GOP

Ted Cruz

0 %

$ 0.001

 

1,000.00

0.00

GOP

Mitt Romney

0 %

$ 0.001

 

1,000.00

0.00

GOP

       
Democratic Nomination:

Outcome

Market

Derived Bet fair Price

Derived Predict It Price

Bet fair Back

Bet fair Lay

Bernie Sanders

34 %

$ 0.336

$ 0.380

2.82

2.92

Joe Biden

29 %

$ 0.294

$ 0.335

3.35

3.40

Michael Bloomberg

12 %

$ 0.116

$ 0.125

8.40

8.60

Elizabeth Warren

8 %

$ 0.077

$ 0.085

13.00

14.00

Pete Buttigieg

5 %

$ 0.053

$ 0.055

19.00

21.00

Andrew Yang

1 %

$ 0.014

$ 0.065

65.00

80.00

Amy Klobuchar

1 %

$ 0.008

$ 0.025

80.00

120.00

Tulsi Gabbard

0 %

$ 0.003

 

310.00

400.00

Kamala Harris

0 %

$ 0.001

$ 0.005

660.00

720.00

Cory Booker

0 %

$ 0.001

$ 0.005

1,000.00

0.00

Sherrod Brown

0 %

$ 0.001

$ 0.005

1,000.00

0.00

John Hickenlooper

0 %

$ 0.001

 

1,000.00

0.00

Julian Castro

0 %

$ 0.001

 

1,000.00

0.00

Kirsten Gillibrand

0 %

$ 0.001

$ 0.005

1,000.00

0.00

Beto O’Rourke

0 %

$ 0.001

$ 0.005

1,000.00

0.00

 

Republican Nomination:

Outcome

Market

Derived Bet fair Price

Derived Predict It Price

Bet fair Back

Bet fair Lay

Donald Trump

94 %

$ 0.935

$ 0.905

1.07

1.08

Nikki Haley

2 %

$ 0.020

$ 0.035

46.00

55.00

Mike Pence

2 %

$ 0.017

$ 0.045

42.00

70.00

Mitt Romney

1 %

$ 0.010

$ 0.025

120.00

990.00

John Kasich

0 %

$ 0.003

$ 0.015

90.00

500.00

Paul Ryan

0 %

$ 0.001

$ 0.005

130.00

1,000.00

Marco Rubio

0 %

$ 0.001

$ 0.005

320.00

1,000.00

Ted Cruz

0 %

$ 0.001

$ 0.005

150.00

1,000.00

Tom Cotton

0 %

$ 0.001

$ 0.005

520.00

0.00

 

Each ad has targeted a specific audience segment according to voter registration status and a mixture of demographic and psychographic factors. For instance, a voter who recently moved right might get a notification to register in a new state. Conversely, a voter might see more of such ads as the election dates are reaching their deadlines. 
How can data change the result of the elections?  
Apart from encouraging voters to increase the election turnout, election analytics offers a game-changing value proposition persuading voters to support a particular candidate. 
The candidates may then use a combination of video ads, banner ads, email campaigns, sponsored social media campaigns, and sponsored social media posts to convince voters to vote for them. 
Changing a person’s leanings is extremely difficult but swaying undecided voters or mobilizing the election campaign can effectively affect the minds of voters, thus can completely transform the outcome of elections. 
To assemble a full picture of potential candidate votes, data scientists aggregate data from numerous sources including third-party vendors, voter registration organizations, and state-run databases, and maintained by the secretary of the state. 
Many industry experts say that voters’ behaviour can be distinguished by “digital identifiers” through their mobile identification number rather than their personal information. The data that they work with is quite sensitive, and they never connect with the actual person in real life. Privacy is the main priority when it comes to handling the election data. 
Having access to personal data of voters from their smartphones provides access to data scientists to gather some marvellous data points such as a list of apps a particular user is using along with real-time analytics. For instance, if a user spends more time on Instagram than Facebook, then it is quite feasible for a candidate to target him her with a creative yet impactful Instagram banner rather than wasting time on Facebook to target any particular of them. 
A very Grubby Business  
The techniques to gather data such as cross-referencing consumer bios from a credit agency with financial data indicating voter’s current income bracket- this data isn’t guaranteed to be accurate. So it is of utmost importance to validate the data before acting upon asking voters to reconfirm some things through surveys. 
They use a bunch of polls in the field and collect data tied to the demographics. And this data then is aggregated with historical data collected over the years where applicable, and each voter is assigned a score indicating the likelihood. They will support the political candidate in the question. 
Finally, they have the data saved all in the mobiles of over 100 million individuals, and they can create an ad for the audience accordingly. If a candidate asks for a particular ad, then they can serve a particular and targeted audience. 
The vast groves of data collected have assisted data scientists in making some crucial changes for the potential voters. For instance, for people who have an app related to some language then it is likely to be republican. They can also build a high pipeline such as voters who support YouTube and billionaire taxes. 
For most of the part, analytics parse out the data at scale and build audience segments that help in mobilizing and converting. Hence, this is why basic techniques like messy data are crucial for data scientists for this kind of work rather than to glean earth-shattering insights about a particular voter. 
Offline and Online Marketing   
Using big data analytics, the election campaign analyzes the demographics of states where they fall behind their opposition. Offline marketing like billboards and television ads are strategically targeted to the audiences that are using big analytics. It also helps in understanding the states where the campaign needs to be improved on the marketing and hence, the settlements of voters. 
Does Data Science help in Election Funding?   
In the USA, the political campaigns are generally funded by donations either from business or some individuals or corporations. But to collect these funds, the campaign needs to find people who have the capacity and a will to donate as well. 
Data Science can help in connecting potential donors with campaigns that they might be willing to support. By tracking essential things like interests, political views, and previous donation amounts and patterns, a campaign can quickly locate who would most likely contribute to the campaign and target them with advertisements, phone calls, emails, or letters requesting to donate some amount as per their capability and desire. These targeted requests can be much more useful than board generic ones. For instance, for a person who usually donates $10 to $50, sending them a request of donation around $100-$1000 would be a waste of time. But compiling the list of potential donors and along with matched or capable amounts, data science can deliver game-changing results and more donations than one has ever received. 
Campaign donations also allow individuals to influence elections in their respective constituencies. For example, you can contribute to a candidate who is running a campaign in another area or even in another state as well. We feel healthy and optimistic about the outcome of the election. With data science, these super donors can be easily targeted, and you can expect results that are beyond one’s expectations. 
Organized Campaigns with Data Science   
Modern elections consist of thousands of volunteers and paid offers made in every state. Again, this is a place where organization and smart use of data can make a huge difference in effectiveness and overall efficiency. 
One way that campaigns use data science is to organize their efforts is to decide how to staff different offices across the country. In states with more competitive races, forecasts and models can be used to estimate how and additional resources can be used best. 
The campaigns also require volunteers. Here too data science to locate and attract those who are likely to sign up the volunteer. These are typically those who are most interested in the issues supported by the candidate and who have time and resources to devote to it. 
Did Data Science Break the Elections?   
Did the use of big analytics and science break elections? As mentioned in the news article of NBC, just like any other tool, data science can be harmful as well as useful for the election campaigns. These are no different than just a form of advertising; the ultimate goal is to find the targeted buyers who are willing to buy your products and retain their loyalty as well.
The problem occurs when the size of the data differs and is used for unethical and dishonest purposes. For example, it can be used to spread conspiracy theories or lies or are likely to believe it. It can be used to spread misinformation about certain groups of people or to foster hate. In the same way, data can be used to sell bogus products or push scams. So ultimately it’s not the data all time or the data science who analyze the data. It’s up to use those who have access to the data and use it most ethically. 
How can one learn Data Science?   
So if you are willing to start a career in Data science right now, data cleaning and institution are a thousand times more important than whatever fancy machine learning they use. But most of the time, you don’t require such techniques. 
In a time of deep political conversations, where social media campaigns have always been accused of promoting echo chambers, discourage dissent while purposing to rule up people who have opposing views.
While some ads do promote incendiary content and political advertising when done right and these can be used to educate voters on causes that matter to them, especially when it comes to voters who have intensely partisan. 
Those who are not from an IT background and looking to jump into the data science industry or related are not required to feel tensed or worried. They can commence their career with mentorship from industry experts and can get placed in top MNC and enterprises. 
A whole bunch of people has quite diverse views when it comes to discussing different issues. They might tend to vote for law and order but are liberal when it comes to healthcare and other economic issues. 

Learn Data Science with SRM University and LEARNXT

If you are looking for a data science course to make real-world decisions like election campaigns, then you check out on the web carefully. Though it has become relatively easy to learn coding languages such as python, Java you got to learn some basic techniques that successful data scientists use to analyze the large amounts and draw conclusions based on it.
Considering the popularity and need for data science among the students and the need for high-quality data science professionals for industry, SRM University in collaboration with LEARNXT offers India’s first accredited Degree in Data Science. Further, the degree offers specialisations in Business Analytics & Machine Learning and Artificial Intelligence, –MSc (Applied Data Science), – PG Diploma (Applied Data Science), and – PG Diploma (Machine Learning and Artificial Intelligence).
Sridhar Nagarajachar (CEO, LEARNXT, SRM Group) commented “SRM Group and LEARNXT believe that we can empower people to achieve their dreams through education. Our learning focuses on knowing, doing and being aspects of skills and careers”.
We have witnessed how data analytics is used in election campaigns and how it affects elections as a whole. And additionally, it has opened an ocean of opportunities on how someone can use technology in their favour with such a significant impact. Stay tuned to LEARNXT to know more about data science.

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