Scinovex
article Open Access

A python programming widely utilized in the development of a twitter bot as a sophisticated advance technical tool

Mayank Kumar TiwariRahul PalVansh ChauhanVinit SinghV.K. SinghS. DhamodaranSwati Sharma

Abstract

This abstract explores the utilization of Python programming as a pivotal tool in the development of advanced Twitter bots. With the rise of social media automation, Twitter bots have become indispensable for tasks ranging from content curation to real-time data analysis and engagement. Python, known for its simplicity, versatility, and robust libraries, emerges as the preferred programming language for crafting sophisticated Twitter bots. The paper delves into the technical intricacies involved in creating Twitter bots using Python, focusing on key aspects such as data scraping, natural language processing (NLP), sentiment analysis, and machine learning integration. These functionalities enable bots to gather relevant information from tweets, analyze user sentiments, and respond intelligently based on predefined criteria or learning algorithms. The bot's functionalities are authenticated using Twitter API keys and access tokens, with robust error handling mechanisms implemented for smooth operation. Through careful analysis of engagement metrics and audience insights, the bot aims to enhance user engagement, drive content curation, and provide a personalized experience for followers. This research article focus on the basic introduction for python programming involved in the twitter bot development, their scope and importance, pros and cons, methodology/implementation and results & analysis in the brief with the code of involving as python programming.

Advanced Malware Detection TechniquesSpam and Phishing DetectionNetwork Security and Intrusion DetectionPython (programming language)Computer scienceProgramming languageSoftware engineeringWorld Wide WebData science

Funding

  • National Institute for Materials Science
Citations
2
FWCI
0.70
field-weighted impact
References
17
Percentile
66%
vs. same field & year
Citations per year
Citation Network

How this paper connects to the literature. Drag to explore, click any node to open that paper.