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Voice of Citizen

Fake News Detection
NLP based Text Analysis
Intelligent Citizen Engagement – CI360

Problem Statement

The term ‘fake news’ refers to misinformation spreading in the country through word of mouth and traditional media as well as more recently through digital methods of communication including edited videos, memes, and unconfirmed advertisements spread through social media.

The authenticity of information is a longstanding concern for businesses issue affecting businesses and society, society, regardless of whether it is printed or digital. Using social networks, the reach and effects of information spread occur at such a Rapid pace, and pace and so amplified That even the most distorted, inaccurate, or false of information can have real-world consequences, within minutes for millions of users. Recently, several public concerns regarding this issue, and some approaches to mitigate the problem were expressed.

Value Proposition

The main objective is to detect fake news, which is a classic text classification problem with a straight forward proposition. It necessary to build a model that can differentiate between “Real” news, and “Fake” news, and to identify the sources that simultaneously publish fake news.
For Different points of view:
  • Citizens- Citizens use this tool to identify fake news or they will see the source of fake news and take alert on email the content of fake news.
  • Government- Concern government Authority will use this model to identify the source of the fake news and take immediate action.
  • Publishers- They will identify if someone using his broadcast name for unauthenticated propaganda.

Solution Strategy

Data Crawling & Scrubbing
  • Data Crawling, Parsing & Filtering
  • Linguistic features, Feature Modelling
  • Robust Metadata based Repository
Statistical Modelling
  • Naïve Bayes
  • Logistics Regression
  • SVM
  • Stochastic Gradient Classifier
  • Random Forest Classifier
Intelligent Analytics
  • Sentiment tonality such as Positive, Negative and Neutral based on the polarity of the words.
  • identify trends or emotional changes.
  • Sentimental KPIs & Graphical Representation

Problem Statement

Citizens use a variety of channels to communicate, including various Grievance Portals, Social Media Posts, Blogs, newspapers, etc. These are all unstructured & semi-structured data. The challenge lies in identifying and categorizing citizen feedback and data. This feedback is intended to understand whether citizens’ attitude and opinion towards a department, scheme, or other section are positive, negative, or neutral.

Value Proposition

Analyzes large volumes of unstructured data by using predefined templates, machine-learning methods, and Natural Language Processing (NLP) to identify deeper insights and sentiments.
Core Sources of data Crawled & Analysed –
  • Grievance Portal
  • Social Media Posts / Tweets
  • Blogs/ Articles
  • News Papers

Solution Strategy

Data Crawling & Scrubbing
  • Data Crawling
  • Data Parsing & Filtering
  • Robust Metadata based Repository
Content/Topic Categorization
  • Group Related Topics
  • NLP based Categorization
  • ML based new topic build
Sentiment Analytics
  • Sentiment tonality such as Positive, Negative, and Neutral.
  • Sentimental KPIs
  • Graphical Representation

Problem Statement

Key Requirement – Campaign Management Solution needs to create Campaigns for citizens. With the help of campaign output, various surveys are conducted, and feedback is collected.

The solution should provide information about citizen’s enrolment into various Schemes which are linked to Bhamashah Card like PENSION scheme, RATION scheme, BSBY scheme, and NREGA scheme.

Value Proposition

Intelligent Citizen Engagement modules is made-for-marketing interface along with automated, trackable, easy-to-repeat processes, analytical capabilities that enable users to turn

  • Citizen insight into highly targeted segments
  • Custom information mapping technology
  • Enables campaign designers to access enterprise-wide data,
  • Ability to customize the entire campaign process to fit Government needs.

Solution Strategy

Citizen Survey
  • Citizen survey Q&A Prep.
  • Forms Creation
  • Feedback collection Mechanism
Data Model Design
  • ETL Development
  • Designing Customer database
  • Design Information Maps
Campaign Execution & Analysis
  • Campaign Execution
  • Response Analysis
  • Update & Scoring Model
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