Unraveling the Dynamics of Television Debates and Social Media Engagement: Insights from an Indian News Show

The relationship between television shows and social media has become increasingly intertwined in recent years. Social media platforms, particularly Twitter, have emerged as significant sources of public opinion and discourse on topics discussed in television shows. In India, news debates leverage the popularity of social media to promote hashtags and engage users in discussions and debates on a daily basis. This paper focuses on the analysis of one of India's most prominent and widely-watched TV news debate shows: "Arnab Goswami-The Debate". The study examines the content of the show by analyzing the hashtags used to promote it and the social media data corresponding to these hashtags. The findings reveal that the show exhibits a strong bias towards the ruling Bharatiya Janata Party (BJP), with over 60% of the debates featuring either pro-BJP or anti-opposition content. Social media support for the show primarily comes from BJP supporters. Notably, BJP leaders and influencers play a significant role in promoting the show on social media, leveraging their existing networks and resources to artificially trend specific hashtags. Furthermore, the study uncovers a reciprocal flow of information between the TV show and social media. We find evidence that the show's choice of topics is linked to social media posts made by party workers, suggesting a dynamic interplay between traditional media and online platforms. By exploring the complex interaction between television debates and social media support, this study contributes to a deeper understanding of the evolving relationship between these two domains in the digital age. The findings hold implications for media researchers and practitioners, offering insights into the ways in which social media can influence traditional media and vice versa.Comment: Accepted at ICWSM 2024. Please cite the ICWSM versio

Personality Detection and Analysis using Twitter Data

Personality types are important in various fields as they hold relevant information about the characteristics of a human being in an explainable format. They are often good predictors of a person's behaviors in a particular environment and have applications ranging from candidate selection to marketing and mental health. Recently automatic detection of personality traits from texts has gained significant attention in computational linguistics. Most personality detection and analysis methods have focused on small datasets making their experimental observations often limited. To bridge this gap, we focus on collecting and releasing the largest automatically curated dataset for the research community which has 152 million tweets and 56 thousand data points for the Myers-Briggs personality type (MBTI) prediction task. We perform a series of extensive qualitative and quantitative studies on our dataset to analyze the data patterns in a better way and infer conclusions. We show how our intriguing analysis results often follow natural intuition. We also perform a series of ablation studies to show how the baselines perform for our dataset.Comment: Submitted to ASONAM 202

Bacteria-inducing legume nodules involved in the improvement of plant growth, health and nutrition

Bacteria-inducing legume nodules are known as rhizobia and belong to the class Alphaproteobacteria and Betaproteobacteria. They promote the growth and nutrition of their respective legume hosts through atmospheric nitrogen fixation which takes place in the nodules induced in their roots or stems. In addition, rhizobia have other plant growth-promoting mechanisms, mainly solubilization of phosphate and production of indoleacetic acid, ACC deaminase and siderophores. Some of these mechanisms have been reported for strains of rhizobia which are also able to promote the growth of several nonlegumes, such as cereals, oilseeds and vegetables. Less studied are the mechanisms that have the rhizobia to promote the plant health; however, these bacteria are able to exert biocontrol of some phytopathogens and to induce the plant resistance. In this chapter, we revised the available data about the ability of the legume nodule-inducing bacteria for improving the plant growth, health and nutrition of both legumes and nonlegumes. These data showed that rhizobia meet all the requirements of sustainable agriculture to be used as bio-inoculants allowing the total or partial replacement of chemicals used for fertilization or protection of crops

Raman and Mössbauer spectroscopic studies of tungsten doped Ni–Zn nano ferrite

In this study, tungsten substituted Ni-Zn nano ferrites of the composition Ni0.5Zn0.5WxFe2−xO4 with x = 0.0, 0.2, 0.4 have been synthesized by a co-precipitation method. The prepared samples were pre-sintered at 850 °C and then annealed at 1000 °C for 3 h each. The structural, morphological, optical and magnetic properties of these samples were studied by using X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy (RS) and Mössbauer spectroscopy (MS). XRD revealed the formation of spinel single-phase structure with an average crystallite size of 53–60 nm. Fourier transform infrared spectroscopy show two prominent peaks primarily due to the tetrahedral and octahedral stretching vibrations in the range of 400–600 cm−1. Raman spectra indicate first order three Raman active modes; (A1 g + Eg + T2 g) at around 688, 475 and 326 cm−1. Mössbauer spectroscopy reveals that substitution of W3+ for Fe3+ cation results in reduction of total magnetic moment and consequently the net magnetization

Unraveling the Dynamics of Television Debates and Social Media Engagement: Insights from an Indian News Show

The relationship between television shows and social media has become increasingly intertwined in recent years. Social media platforms, particularly Twitter, have emerged as significant sources of public opinion and discourse on topics discussed in television shows. In India, news debates leverage the popularity of social media to promote hashtags and engage users in discussions and debates on a daily basis. This paper focuses on the analysis of one of India's most prominent and widely-watched TV news debate shows: 'Arnab Goswami -- The Debate'. The study examines the content of the show by analyzing the hashtags used to promote it and the social media data corresponding to these hashtags. The goal is to understand the composition of the audience engaged in social media discussions related to the show. The findings reveal that the show exhibits a strong bias towards the ruling Bharatiya Janata Party (BJP), with over 60% of the debates featuring either pro-BJP or anti-opposition content. Social media support for the show primarily comes from BJP supporters. Notably, BJP leaders and influencers play a significant role in promoting the show on social media, leveraging their existing networks and resources to artificially trend specific hashtags. Furthermore, the study uncovers a reciprocal flow of information between the TV show and social media. We find evidence that the show's choice of topics is linked to social media posts made by party workers, suggesting a dynamic interplay between traditional media and online platforms. By exploring the complex interaction between television debates and social media support, this study contributes to a deeper understanding of the evolving relationship between these two domains in the digital age. The findings hold implications for media researchers and practitioners, offering insights into the ways in which social media can influence traditional media and vice versa