A Review of the House of Representatives Election Analysis in Broad Listening

https://www.youtube.com/live/C_f8pa9XhqM

It was a pretty interesting story, so I'll pick up the transcript and comments later.

Project Summary

Collaborating with Nippon Television Network Corporation to utilize broad listening technology for election coverage of the House of Representatives elections

Analyzes comments on X (formerly Twitter) to visualize opinions about the election

Produce all four types of reports (overall election, policy, each party, and same-day preliminary report)

Technical Challenges and Improvements

TTTC system improvements (UI improvements, faster processing speed, etc.)

Spam filtering implementation

Addition of a favorites function

Implementation of smartphone support

Main Results

Utilize both terrestrial and digital distribution

Six digital programs, six hours of live broadcast on election day

Real-time public opinion analysis and visualization

Challenges and Learning

Ensuring Accuracy (Importance of Fact Checking)

Support for large-scale data processing

Cooperation with broadcasters

Future Outlook

Improved clustering methods

Fact-checking automation study

Possibility of utilizing a wider range of data sources

All participants appreciated that the project was an important step in the convergence of the broadcasting industry and technology.

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2024-10-26

Widely heard on TV programs!

https://pbs.twimg.com/media/GayYD9zaAAESuKM?format=jpg&name=medium#.png

NTV's "news zero" is rolling out its innovative "Broad Listening" project, led by AI engineer Takahiro Anno, just in time for the House of Representatives election.

Broad Listening uses AI-powered analysis to visualize over 38,000 election-related posts from X/Twitter, clustering similar opinions and highlighting key voter arguments about policy, candidates, and social issues. (Using AI Objectives Institute's "Talk to the City" tool)

This approach breaks the filter bubble and helps both the public and politicians see opinions in a broader perspective, including those that are often overlooked. For more information, please visit NTV's website.

Right side is the favorite pane.

The Anno team has released an enhanced version of "Talk to the City" for a recent TV broadcast program. Features such as favorites, mobile-friendly UI, and cluster commenting accuracy have been improved.

On the right is my favorite pane.

https://pbs.twimg.com/media/Gaz9CmOaMAAwjkZ?format=jpg&name=medium#.png

Click here to read the Anno team's blog.

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claude.icon

Japan's terrestrial broadcasters have demonstrated a new challenge to the digitalization of democracy.

~A Breakthrough in Election Reporting x Collective Intelligence Analysis~.

Introduction.

NTV, a major commercial TV station, has implemented a groundbreaking initiative for Japan's 2024 House of Representatives election. The project combines conventional election reporting with the latest AI technology to visualize the voices of voters on social media in real time. This attempt offers new insights into the nature of democracy in the digital age.

Project Innovation

The project is notable for its attempt by traditional media to leverage the "collective intelligence" of the digital space. Specifically, election-related posts on social media X (formerly Twitter) were analyzed in real time using AI technology. The results were provided to viewers through both terrestrial broadcasting and digital distribution.

Technical Features

The core technology is a technique known as "broadcast listening. This is the opposite concept of "broadcast (one-to-many information dissemination)," in which the opinions of many people are aggregated and presented in an organized form. It is based on the open source software "talk to the city" and has been realized with unique improvements.

Implementation and Results

The project conducted the following four types of analysis

Public opinion on the election as a whole

Trends in Policy Discussion

Responses to each political party

Real-time reactions during the vote count

Particularly noteworthy was the effort on the day of the vote count. In just 25 minutes after the polls opened, 60,000 submissions were collected and analyzed, and the results of the analysis were successfully provided to viewers 85 minutes later. This demonstrated the potential for highly immediate election coverage in the digital age.

Challenges and Prospects

The project also identified several important issues:

Representativeness of opinions on digital platforms

Need for automated fact-checking

Data quality control and privacy protection

At the same time, however, these challenges are important considerations in the future development of digital democracy.

Conclusion.

This attempt deserves attention as an example of how the fusion of traditional media and digital technology opens up new possibilities for democracy. In particular, the effort to visualize voters' voices in real time will serve as a model case for future digitalization of election coverage and democracy.

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TV Broadcasting x AI: An Experiment in Democratic Digitization from Japan

～Behind the Scenes of the World's First AI-Based Election Reporting by a Terrestrial TV Station

summary

During Japan's October 2024 lower house election, NTV, a major Japanese broadcaster, implemented a groundbreaking AI-based election coverage project. Called "Broad Listening," the technology analyzed and visualized tens of thousands of posts on social media in real time.

Technical characteristics of the project

Data collection and analysis systems

Analysis of: election-related posts on X (formerly Twitter)

Collection size: 1 million posts (budget: approximately $5,000)

Analysis period: 3 periods: pre-election period, first half, and second half

Real-time analysis: 60,000 cases processed in 25 minutes on the day of the vote count

Analysis Category

Overall Election Trend Analysis

Trend Analysis of Policy Discussion

Reaction analysis by political party

Real-time analysis during preliminary vote counting

Technical improvements

UI/UX Improvements

Implementation of a favorites function

Smartphone and tablet support

Optimization of visibility for broadcast

Improvement of analysis accuracy

Improved clustering algorithm

Optimization of the method for extracting representative comments

Spam filtering implementation

Important Points for Implementation

system requirement

Tier 5 level processing power of GPT API

Stable processing up to 200 parallels

Support for 7-day data acquisition limit

Establishment of 24-hour operation system

Quality Control Process

Primary filtering by AI

Manual fact-checking

Processing into broadcast content

Final confirmation process

Operation System

Engineering Team: System Development and Maintenance

Editorial Team: Content Creation

Broadcast station team: Quality control and broadcast support

Project management: Daily progress review meetings

Findings

technical side

Importance of using and improving open source software

Effects of Parallelization in Real-Time Processing

Need for continuous improvement of UI/UX

practical side

Importance of close cooperation with broadcasters

Establishment of fact-checking system

Maintain flexible improvement cycles

Future Outlook

Further improvement of clustering methods

Consideration of fact-checking automation

Possibility of multi-platform support

Conclusion.

This project was an important practical example of the integration of conventional media and AI technology. In particular, the fact that the project was based on open source software that was modified for actual broadcasting applications will serve as a valuable reference case for similar projects in the future.

Schedule to be on TV until 26:00 and then immediately to the US the next morning.

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