Social media platforms have become an open door for users to share their views, resulting in a growing trend of offensive content being shared on social media. Detecting and addressing offensive content is crucial due to its significant impact on society. Although there has been extensive research on the detection of offensive content in the English language, there is a notable gap in detecting offensive content in multimodal settings involving code-mixed languages. In this article, we propose a large scale multimodal code-mixed dataset for Hinglish (Hindi+English)MultiSEAO-Mixfocusing on women and children. TheMultiSEAO-Mixis annotated with offensiveness, sentiment, emotion, and their respective intensities. Additionally, it is also annotated with author support. A multimodal, multitask framework is proposed that considers offensive detection, intensity prediction, and author support as the primary tasks and improves their performance using sentiment, emotion, and corresponding intensities as the auxiliary tasks. Further, we propose a fusion technique that captures the enhanced multimodal representation to improve the performance of our model. Experimental results demonstrate that the proposed multitask framework improves the model performance by more than 4.5 points compared to multitask system without sentiment and emotion as the auxiliary tasks.

Due to its growing impact on public opinion, hate speech on social media has garnered increased attention.While automated methods for identifying hate speech have been presented in the past, they have mostly been limited to analyzing textual content.The interpretability of such models has received very little attention, despite the social and legal consequences of erroneous predictions.In this work, we present a novel problem of Distress Identification and Cause Extraction (DICE) from multimodal online posts.We develop a multi-task deep framework for the simultaneous detection of distress content and identify connected causal phrases from the text using emotional information.The emotional information is incorporated into the training process using a zero-shot strategy, and a novel mechanism is devised to fuse the features from the multimodal inputs.Furthermore, we introduce the first-ofits-kind Distress and Cause annotated Multimodal (DCaM) dataset of 20,764 social media posts.We thoroughly evaluate our proposed method by comparing it to several existing benchmarks.Empirical assessment and comprehensive qualitative analysis demonstrate that our proposed method works well on distress detection and cause extraction tasks, improving F1 and ROS scores by 1.95% and 3%, respectively, relative to the best-performing baseline.The code and the dataset can be accessed from the following link: https://www.iitp. ac.in/~ai-nlp-ml/resources.html#DICE.

Hybrid sensor networks comprise of mobile and static sensor nodes setup for the purpose of collaboratively performing tasks like sensing a phenomenon or monitoring a region. In this paper, we present a novel approach for navigating a mobile sensor node (MSN) through such a hybrid sensor network. The static sensor nodes in the sensor network guide the MSN to the phenomenon. One or more MSN’s are selected based on their proximity to the detected phenomenon. Navigation is accomplished using the concepts of credit based field setup and navigation force from static sensor nodes. Our approach does not require any prior maps of the environment thus, cutting down the cost of the overall system. The simulation results have verified the effectiveness of the proposed approach. In each of the simulation runs, the static sensor nodes were able to successfully guide the MSN towards the phenomenon.