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  Digital Catalogue for Technology and Products Development


   Technology and Product Development

    Basic Information

Technology developed: Fake News Detection App (TP19763740287)
Category: Technology Service/Know how
Details of Inventor(s):
Inventor Institution/Organization/Company Department Designation
Kushal Shah Indian Institute of Science Education and Research (IISER) Bhopal Electrical Engineering & Computer Science Associate Professor
Mohd. Rameez Qureshi Indian Institute of Science Education and Research (IISER) Bhopal Electrical Engineering & Computer Science Project Lab Assistant
Rajakrishnan Rajkumar Indian Institute of Science Education and Research (IISER) Bhopal Humanities & Social Sciences Assistant Professor
Technical Application Area: Artificial Intelligence & Machine Learning
If 'Other', please specify:
Please give more details of new technical application area:
Organization(s):
Indian Institute of Science Education and Research (IISER) Bhopal
Affiliated Ministry: MHRD, Govt. of India
Type of technology development: Indigenous
Does the technology help in replacing any import items currently
procured from outside India?
No
Does the technology have export potential? Yes
Category of Technology developed: Immediate Deployment
Stage of Development: Prototype Level
Please describe in detail including the TRL Level:
TRL Level : 4 Our algorithm has already been tested to work on large datasets with high accuracy and already published: https://www.aclweb.org/anthology/W19-3409 A Simple Approach to Classify Fictional and Non-Fictional Genres M. R. Qureshi, S. Ranjan, Rajakrishnan P. R. and K. Shah, StoryNLP @ ACL 2019, 81-89 We have also developed an App, called NewsChase, based on this algorithm to help users in keeping away from fake news. This App is freely available on Google Play Store: https://play.google.com/store/apps/details?id=com.iiserb.fictonews The App has been tested under laboratory conditions and over a few users. Now, a large scale testing is required to ensure its effectiveness.

    Abstract:

Applications: Fake news has become a huge menace in todays world and this problem comes in two varieties. One kind of fake news is where the information is factually incorrect, and another kind is where the article is written in an emotionally manipulative language in order to unethically boost readership. The second kind of fake news is as dangerous as the first kind and can lead to unwanted societal conflicts. Our AI based App, NewsChase, finds out using Machine Learning if a news article uses manipulative language, and gives a warning to the reader so that she or he can be careful while reading such articles. This App is based on our Fictometer work recently published at StoryNLP at ACL 2019. Fictometer measures the imaginative writing styles in a given English text, using a Machine Learning algorithm. Texts which are more imaginative than a certain threshold are classified as Fiction, when applied to news articles, this helps in detecting the second kind of fake news as described above, and those below it are classified as Nonfiction. Interestingly, it turns out that this classification can be done by using only two parameters in our Machine Learning algorithm. One is the ratio of the number of adverbs and adjectives used in the article and another is the ratio of the number of adjectives and pronouns. We have found through rigorous testing and analysis that fiction texts usually have a much higher value of Adverb by Adjective ratio, and nonfiction texts have a much higher value of Adjective by Pronoun ratio. Please note that the algorithm does not check for factual information, and only analyses the writing style.
Advantages: Classification of English texts is largely a solved problem and there are algorithms already available to achieve this with high accuracy. However, these algorithms are usually very resource intensive and also work like a black box, i.e. they do not offer any insights into the features responsible for doing the classification. This makes it very difficult to deploy these algorithms on a large scale. Our algorithm, on the other hand, is very light, fast and also gives accurate insights into the features responsible for labelling a given piece of English text as manipulative or fake. We believe our work is a significant contribution towards the general goal of Explainable AI.

    Technology Inputs:

Imported Equipment/Spare Parts:
Equipment/Spare Parts Year ITC-HS Code
NA
Indigenous Equipment/Spare Parts:
Equipment/Spare Parts Year ITC-HS Code
NA
Imported Raw Materials:
Raw Materials Year ITC-HS Code
NA
Indigenous Raw Materials:
Raw Materials Year ITC-HS Code
NA
Existing R&D Facilities used:
Facilities Year ITC-HS Code
NA

    R&D Investment:

R&D investment (Rs. in Lakhs):                    
Indian Source (Rs.) Foreign Source (Rs.) Year
0 0 NA

   Patents & Publications:

Patents:
Filed Patents (No.) Granted Patents (No.) Year
0 0 NA
Publications:
Submitted (No.) Published (No.) Year
0 1 NA

    Commercialization Potential:

Who are the Potential Licensees?
What commercially available products address
the same problem?
Company Product Problem Addressed
Would you like to develop this invention further with
corporate research support?
Yes
Would you be interested in participating in cluster based
programs for commercialization research or business
planning for your invention?
Yes
      Submitted by: Sarita Panwar Date of Submission: 5-8-2020



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