Do Public and Government Think Similar About Indian Cleanliness Campaign?

Dhiman, Aarzoo; Toshniwal, Durga

doi:10.1007/978-3-030-11680-4_34

Cited by 1 publication

(2 citation statements)

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“…To overcome the limitation of inconsistency in location found from user-proile and the textual content of tweet, we extract the location information from the tweets using Named Entity Recognition (NER) and partial keyword matching [14]. The NER module provided with the python NLTK 3 library helps in facilitating the labeling of keywords into corresponding Part-of-Speech (POS) and locate and classify the named entities in a sentence.…”

Section: Location Extraction From Tweetsmentioning

confidence: 99%

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A Twitter Framework to assess the Effectiveness of Indian Government Campaign

Dhiman

Toshniwal

2022

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Many government and private agencies introduce social, governmental, educational, commercial and public-health based campaigns to refine various sections of the society. Traditionally, citizen feedback and on-field surveys are performed to assess the effectiveness of such campaigns. However, there is limited availability of the social media tools that assess the impact of different government campaigns automatically. In this research work, a framework has been proposed which uses the social media data i.e. Twitter data pertaining to an Indian Cleanliness Campaign Swachh Bharat Abhiyan (SBA) to perform the effectiveness assessment. This research work has been performed in two parts. First, Twitter data has been processed to predict the perceptions of citizens using Word Embeddings -based Tweet Pooling and Subjectivity score -based Sentiment Analysis and second, the performance of the cities has been predicted using the demographic features based predictor models. The experimentation shows a 0.77 correlation between the proposed framework and the government surveys while predicting the citizens’ perspectives and 80(+/-15)% accuracy while predicting the performance of cities using Random Forest Regression. Furthermore, the cities have been clustered using Twitter and the demographic data to find out the interesting patterns and behaviors. This research work provides better insights to the potential of social media data in the interventional studies of the developing countries such as India.

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Section: Location Extraction From Tweetsmentioning

confidence: 99%

“…So, the tweet texts have been categorized into government generated tweets, which includes the tweets posted by using these dedicated handles and public generated tweets, which includes all the rest of the tweets. These dedicated handles were spotted from the corpus using the keyword search [14].…”

Section: Location Extraction From Tweetsmentioning

confidence: 99%