Purpose: The purpose of this paper is to explore the dark side of news community forums: the proliferation of opinion manipulation trolls. In particular, it explores the idea that a user who is called a troll by several people is likely to be one. It further demonstrates the utility of this idea for detecting accused and paid opinion manipulation trolls and their comments as well as for predicting the credibility of comments in news community forums. Design/methodology/approach: The authors are aiming to build a classifier to distinguish trolls vs regular users. Unfortunately, it is not easy to get reliable training data. The authors solve this issue pragmatically: the authors assume that a user who is called a troll by several people is likely to be such, which are called accused trolls. Based on this assumption and on leaked reports about actual paid opinion manipulation trolls, the authors build a classifier to distinguish trolls vs regular users. Findings: The authors compare the profiles of paid trolls vs accused trolls vs non-trolls, and show that a classifier trained to distinguish accused trolls from non-trolls does quite well also at telling apart paid trolls from non-trolls. Research limitations/implications: The troll detection works even for users with about 10 comments, but it achieves the best performance for users with a sizable number of comments in the forum, e.g. 100 or more. Yet, there is not such a limitation for troll comment detection. Practical implications: The approach would help forum moderators in their work, by pointing them to the most suspicious users and comments. It would be also useful to investigative journalists who want to find paid opinion manipulation trolls. Social implications: The authors can offer a better experience to online users by filtering out opinion manipulation trolls and their comments. Originality/value: The authors propose a novel approach for finding paid opinion manipulation trolls and their posts.

Effective modeling of human behavior is crucial for the safe and reliable coexistence of humans and autonomous vehicles. Traditional deep learning methods have limitations in capturing the complexities of pedestrian behavior, often relying on simplistic representations or indirect inference from visual cues, which hinders their explainability. To address this gap, we introduce PedVLM, a vision-language model that leverages multiple modalities (RGB images, optical flow, and text) to predict pedestrian intentions and also provide explainability for pedestrian behavior. PedVLM comprises a CLIP-based vision encoder and a text-to-text transfer transformer (T5) language model, which together extract and combine visual and text embeddings to predict pedestrian actions and enhance explainability. Furthermore, to complement our PedVLM model and further facilitate research, we also publicly release the corresponding dataset, PedPrompt, which includes the prompts in the Question-Answer (QA) template for pedestrian intention prediction. PedVLM is evaluated on PedPrompt, JAAD, and PIE datasets demonstrates its efficacy compared to state-of-the-art methods. The dataset and code will be made available at https://github.com/munirfarzeen/PedVLM.