Gullal S. Cheema

selected publications

1. ICMR

   Identification of Speaker Roles and Situation Types in News Videos

   Gullal S. Cheema, Judi Arafat, Chiao-I Tseng, and 3 more authors

   2024

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   The proliferation of news sources on the web amplifies the problem of disinformation and misinformation, impacting public perception and societal stability. These issues necessitate the identification of bias in news broadcasts, whereby the analysis and understanding of speaker roles and news contexts are essential prerequisites. Although there is prior research on multimodal speaker role recognition (mostly) in the news domain, modern feature representations have not been explored yet, and no comprehensive public dataset is available. In this paper, we propose novel approaches to classify speaker roles (e.g., "anchor," "reporter," "expert") and categorise scenes into news situations (e.g., "report," "interview") in news videos, to enhance the understanding of news content. To bridge the gap of missing datasets, we present a novel annotated dataset for various speaker roles and news situations from diverse (national) media outlets. Furthermore, we suggest a rich set of features and employ aggregation and post-processing techniques. In our experiments, we compare classifiers like Random Forest and XGBoost for identifying speaker roles and news situations in video segments. Our approach outperforms recent state-of-the-art methods, including end-to-end multimodal deep network and unimodal transformer-based models. Through detailed feature combination analysis, generalisation and explainability insights, we underscore our models’ capabilities and set new directions for future research.

2. Frontiers

   Understanding Image-Text Relations and News Values for Multimodal News Analysis

   Gullal S. Cheema, Sherzod Hakimov, Eric Müller-Budack, and 3 more authors

   Frontiers in Artificial Intelligence 2023

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   The analysis of news dissemination is of utmost importance since the credibility of information and the identification of disinformation and misinformation affect society as a whole. Given the large amounts of news data published daily on the Web, the empirical analysis of news with regard to research questions and the detection of problematic news content on the Web require computational methods that work at scale. Today’s online news are typically disseminated in a multimodal form, including various presentation modalities such as text, image, audio, and video. Recent developments in multimodal machine learning now make it possible to capture basic “descriptive” relations between modalities–such as correspondences between words and phrases, on the one hand, and corresponding visual depictions of the verbally expressed information on the other. Although such advances have enabled tremendous progress in tasks like image captioning, text-to-image generation and visual question answering, in domains such as news dissemination, there is a need to go further. In this paper, we introduce a novel framework for the computational analysis of multimodal news. We motivate a set of more complex image-text relations as well as multimodal news values based on real examples of news reports and consider their realization by computational approaches. To this end, we provide (a) an overview of existing literature from semiotics where detailed proposals have been made for taxonomies covering diverse image-text relations generalisable to any domain; (b) an overview of computational work that derives models of image-text relations from data; and (c) an overview of a particular class of news-centric attributes developed in journalism studies called news values. The result is a novel framework for multimodal news analysis that closes existing gaps in previous work while maintaining and combining the strengths of those accounts. We assess and discuss the elements of the framework with real-world examples and use cases, setting out research directions at the intersection of multimodal learning, multimodal analytics and computational social sciences that can benefit from our approach.

3. NAACL

   MM-Claims: A Dataset for Multimodal Claim Detection in Social Media

   Gullal S. Cheema, Sherzod Hakimov, Abdul Sittar, and 3 more authors

   Jul 2022

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   In recent years, the problem of misinformation on the web has become widespread across languages, countries, and various social media platforms. Although there has been much work on automated fake news detection, the role of images and their variety are not well explored. In this paper, we investigate the roles of image and text at an earlier stage of the fake news detection pipeline, called claim detection. For this purpose, we introduce a novel dataset, MM-Claims, which consists of tweets and corresponding images over three topics: COVID-19, Climate Change and broadly Technology. The dataset contains roughly 86000 tweets, out of which 3400 are labeled manually by multiple annotators for the training and evaluation of multimodal models. We describe the dataset in detail, evaluate strong unimodal and multimodal baselines, and analyze the potential and drawbacks of current models.

4. BIGMM

   Semi-supervised clustering with neural networks

   Ankita Shukla, Gullal S. Cheema, and Saket Anand

   Jul 2020

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   Clustering using neural networks has recently demonstrated promising performance in machine learning and computer vision applications. However, the performance of current approaches is limited either by unsupervised learning or their dependence on large set of labeled data samples. In this paper, we propose ClusterNet that uses pairwise semantic constraints from very few labeled data samples (<5% of total data) and exploits the abundant unlabeled data to drive the clustering approach. We define a new loss function that uses pairwise semantic similarity between objects combined with constrained k-means clustering to efficiently utilize both labeled and unlabeled data in the same framework. The proposed network uses convolution autoencoder to learn a latent representation that groups data into k specified clusters, while also learning the cluster centers simultaneously. We evaluate and compare the performance of ClusterNet on several datasets and state of the art deep clustering approaches.