Toxicity in digital media poses significant challenges, yet little attention has been given to its dynamics within the rapidly growing medium of podcasts. This paper addresses this gap by analyzing political podcast data to study the emergence and propagation of toxicity, focusing on conversation chains-structured reply patterns within podcast transcripts. Leveraging state-of-the-art transcription models and advanced conversational analysis techniques, we systematically examine toxic discourse in over 30 popular political podcasts in the United States. Our key contributions include: (1) creating a comprehensive dataset of transcribed and diarized political podcasts, identifying thousands of toxic instances using Google's Perspective API, (2) uncovering concerning trends where a majority of episodes contain at least one toxic instance, (3) introducing toxic conversation chains and analyzing their structural and linguistic properties, revealing characteristics such as longer durations, repetitive patterns, figurative language, and emotional cues tied to anger and annoyance, (4) identifying demand-related words like 'want', 'like', and 'know' as precursors to toxicity, and (5) developing predictive models to anticipate toxicity shifts based on annotated change points. Our findings provide critical insights into podcast toxicity and establish a foundation for future research on real-time monitoring and intervention mechanisms to foster healthier discourse in this influential medium.

Recently, social media platforms are heavily moderated to prevent the spread of online hate speech, which is usually fertile in toxic words and is directed toward an individual or a community. Owing to such heavy moderation, newer and more subtle techniques are being deployed. One of the most striking among these is fear speech. Fear speech, as the name suggests, attempts to incite fear about a target community. Although subtle, it might be highly effective, often pushing communities toward a physical conflict. Therefore, understanding their prevalence in social media is of paramount importance. This article presents a large-scale study to understand the prevalence of 400K fear speech and over 700K hate speech posts collected from Gab.com. Remarkably, users posting a large number of fear speech accrue more followers and occupy more central positions in social networks than users posting a large number of hate speech. They can also reach out to benign users more effectively than hate speech users through replies, reposts, and mentions. This connects to the fact that, unlike hate speech, fear speech has almost zero toxic content, making it look plausible. Moreover, while fear speech topics mostly portray a community as a perpetrator using a (fake) chain of argumentation, hate speech topics hurl direct multitarget insults, thus pointing to why general users could be more gullible to fear speech. Our findings transcend even to other platforms (Twitter and Facebook) and thus necessitate using sophisticated moderation policies and mass awareness to combat fear speech.

The past decade has witnessed a rapid increase in technology ownership across rural areas of India, signifying the potential for ICT initiatives to empower rural households. In our work, we focus on the web infrastructure of one such ICT - Digital Green that started in 2008. Following a participatory approach for content production, Digital Green disseminates instructional agricultural videos to smallholder farmers via human mediators to improve the adoption of farming practices. Their web-based data tracker, CoCo, captures data related to these processes, storing the attendance and adoption logs of over 2.3 million farmers across three continents and twelve countries. Using this data, we model the components of the Digital Green ecosystem involving the past attendance-adoption behaviours of farmers, the content of the videos screened to them and their demographic features across five states in India. We use statistical tests to identify different factors which distinguish farmers with higher adoption rates to understand why they adopt more than others. Our research finds that farmers with higher adoption rates adopt videos of shorter duration and belong to smaller villages. The co-attendance and co-adoption networks of farmers indicate that they greatly benefit from past adopters of a video from their village and group when it comes to adopting practices from the same video. Following our analysis, we model the adoption of practices from a video as a prediction problem to identify and assist farmers who might face challenges in adoption in each of the five states. We experiment with different model architectures and achieve macro-f1 scores ranging from 79% to 89% using a Random Forest classifier. Finally, we measure the importance of different features using SHAP values and provide implications for improving the adoption rates of nearly a million farmers across five states in India.

WhatsApp is the most popular messaging app in the world. Due to its popularity, WhatsApp has become a powerful and cheap tool for political campaigning being widely used during the 2019 Indian general election, where it was used to connect to the voters on a large scale. Along with the campaigning, there have been reports that WhatsApp has also become a breeding ground for harmful speech against various protected groups and religious minorities. Many such messages attempt to instil fear among the population about a specific (minority) community. According to research on inter-group conflict, such `fear speech' messages could have a lasting impact and might lead to real offline violence. In this paper, we perform the first large scale study on fear speech across thousands of public WhatsApp groups discussing politics in India. We curate a new dataset and try to characterize fear speech from this dataset. We observe that users writing fear speech messages use various events and symbols to create the illusion of fear among the reader about a target community. We build models to classify fear speech and observe that current state-of-the-art NLP models do not perform well at this task. Fear speech messages tend to spread faster and could potentially go undetected by classifiers built to detect traditional toxic speech due to their low toxic nature. Finally, using a novel methodology to target users with Facebook ads, we conduct a survey among the users of these WhatsApp groups to understand the types of users who consume and share fear speech. We believe that this work opens up new research questions that are very different from tackling hate speech which the research community has been traditionally involved in.