Assoc Prof. Fazle Rabbi
Work Package Co-Leader and Task Leader
University of Bergen
2026
Fatemi, Bahareh; Rabbi, Fazle; Lamo, Yngve; Opdahl, Andreas L.
A Framework for Comparative Analysis of News Content: A Model-Based Approach Journal Article Forthcoming
In: Communications in Computer and Information Science, Springer, Forthcoming.
@article{framewoerk25,
title = {A Framework for Comparative Analysis of News Content: A Model-Based Approach},
author = {Bahareh Fatemi and Fazle Rabbi and Yngve Lamo and Andreas L. Opdahl},
url = {https://mediafutures.no/ccis/},
year = {2026},
date = {2026-08-02},
urldate = {2025-08-02},
journal = {Communications in Computer and Information Science, Springer},
abstract = {In the digital age, the volume of news data available from diverse
sources is vast and continually growing. On the one hand, the quantity of information can overwhelm reporters and on the other hand, news reporting is further complicated by the inherent complexities of multifaceted events that evolve over time, as well as the biases and perspectives that different reporters and media outlets bring to their coverage. Despite such challenges, journalists must report on events in a timely and ethical manner. However, there is a lack of computational methods for analyzing massive news streams in an explainable and responsible way. In this paper, we propose a content based news analysis framework based on news comparison that enables modeling various analytical tasks such as analyzing the perspectives of news publishers, monitoring the progression of news events from various perspectives, exploring the evolution patterns of events over time and analyzing news article variants and for uncovering underlying story-lines. Our approach utilizes a knowledge graph to represent key concepts in the news domain, such as events and their contextual information, across various dimensions. This facilitates a multi-dimensional and comparative analysis of news article variants. We demonstrate the practical applicability of our method through a running example. By adopting a model-based approach, our framework offers the flexibility needed to represent a broad spectrum of domain concepts.},
keywords = {},
pubstate = {forthcoming},
tppubtype = {article}
}
In the digital age, the volume of news data available from diverse
sources is vast and continually growing. On the one hand, the quantity of information can overwhelm reporters and on the other hand, news reporting is further complicated by the inherent complexities of multifaceted events that evolve over time, as well as the biases and perspectives that different reporters and media outlets bring to their coverage. Despite such challenges, journalists must report on events in a timely and ethical manner. However, there is a lack of computational methods for analyzing massive news streams in an explainable and responsible way. In this paper, we propose a content based news analysis framework based on news comparison that enables modeling various analytical tasks such as analyzing the perspectives of news publishers, monitoring the progression of news events from various perspectives, exploring the evolution patterns of events over time and analyzing news article variants and for uncovering underlying story-lines. Our approach utilizes a knowledge graph to represent key concepts in the news domain, such as events and their contextual information, across various dimensions. This facilitates a multi-dimensional and comparative analysis of news article variants. We demonstrate the practical applicability of our method through a running example. By adopting a model-based approach, our framework offers the flexibility needed to represent a broad spectrum of domain concepts.
sources is vast and continually growing. On the one hand, the quantity of information can overwhelm reporters and on the other hand, news reporting is further complicated by the inherent complexities of multifaceted events that evolve over time, as well as the biases and perspectives that different reporters and media outlets bring to their coverage. Despite such challenges, journalists must report on events in a timely and ethical manner. However, there is a lack of computational methods for analyzing massive news streams in an explainable and responsible way. In this paper, we propose a content based news analysis framework based on news comparison that enables modeling various analytical tasks such as analyzing the perspectives of news publishers, monitoring the progression of news events from various perspectives, exploring the evolution patterns of events over time and analyzing news article variants and for uncovering underlying story-lines. Our approach utilizes a knowledge graph to represent key concepts in the news domain, such as events and their contextual information, across various dimensions. This facilitates a multi-dimensional and comparative analysis of news article variants. We demonstrate the practical applicability of our method through a running example. By adopting a model-based approach, our framework offers the flexibility needed to represent a broad spectrum of domain concepts.
2025
Tarekegn, Adane Nega; Rabbi, Fazle; Steskal, Lubos; Tessem, Bjørnar
Automated News Clip Generation via Robust Video Summarization Conference
37th International Conference on Tools with Artificial Intelligence (CITAI2025), 2025.
@conference{automatednews25,
title = {Automated News Clip Generation via Robust Video Summarization},
author = {Adane Nega Tarekegn and Fazle Rabbi and Lubos Steskal and Bjørnar Tessem},
year = {2025},
date = {2025-11-05},
urldate = {2025-11-05},
booktitle = {37th International Conference on Tools with Artificial Intelligence (CITAI2025)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Tarekegn, Adane Nega; Tessem, Bjørnar; Rabbi, Fazle
A New Cluster Validation Index based on Stability Analysis Conference
ICPRAM 2025, 2025.
@conference{clustervalidtio25,
title = {A New Cluster Validation Index based on Stability Analysis},
author = {Adane Nega Tarekegn and Bjørnar Tessem and Fazle Rabbi},
url = {https://mediafutures.no/133091/},
year = {2025},
date = {2025-01-01},
booktitle = {ICPRAM 2025},
journal = {ICPRAM 2025},
abstract = {Clustering is a frequently employed technique across various domains, including anomaly detection, recommender systems, video analysis, and natural language processing. Despite its broad application, validating clustering results has become one of the main challenges in cluster analysis. This can be due to factors such as the subjective nature of clustering evaluation, lack of ground truth in many real-world datasets, and sensitivity of evaluation metrics to different cluster shapes and algorithms. While there is extensive literature work in this area, developing an evaluation method that is both objective and quantitative is still challenging task requiring more effort. In this study, we proposed a new Clustering Stability Assessment Index (CSAI) that can provide a unified and quantitative approach to measure the quality and consistency of clustering solutions. The proposed CSAI validation index leverages a data resampling approach and prediction analysis to assess clustering stabil},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Clustering is a frequently employed technique across various domains, including anomaly detection, recommender systems, video analysis, and natural language processing. Despite its broad application, validating clustering results has become one of the main challenges in cluster analysis. This can be due to factors such as the subjective nature of clustering evaluation, lack of ground truth in many real-world datasets, and sensitivity of evaluation metrics to different cluster shapes and algorithms. While there is extensive literature work in this area, developing an evaluation method that is both objective and quantitative is still challenging task requiring more effort. In this study, we proposed a new Clustering Stability Assessment Index (CSAI) that can provide a unified and quantitative approach to measure the quality and consistency of clustering solutions. The proposed CSAI validation index leverages a data resampling approach and prediction analysis to assess clustering stabil
2024
Rabbi, Fazle
A Model-Based Framework for Exploring Conflict Dynamics Workshop
2024.
@workshop{conflict24,
title = {A Model-Based Framework for Exploring Conflict Dynamics},
author = {Fazle Rabbi},
url = {https://mediafutures.no/3652620-3688206-2/},
year = {2024},
date = {2024-09-24},
issue = {ACM/IEEE },
abstract = {This paper introduces a novel framework for conflict analysis that leverages advanced visual modeling techniques. By employing comparative analysis, key variables influencing armed conflicts are identified and analyzed. The framework includes a meta-model representing domain concepts such as the goals and strategies of conflicting parties, escalating stages, and impacts of conflicts. Conflict escalation is a complex process characterized by interactions between opposing parties. This paper presents a structured model that outlines how conflicts evolve and intensify over time. We adapt a meta-modeling framework called the Diagram Predicate Framework (DPF) to represent conflict-related concepts and extend it to support abstract view generation. This framework facilitates the analysis of conflict trends and the study of dynamics across various levels of abstraction. A computational model based on category theory is proposed for trend analysis, enabling the extraction of patterns of conflict evolution and the comparison of strategies and goals at different escalation stages. Categorical operations such as pullback and limit construction are employed to compute conflict evolution and identify common structures among conflict instances, providing insights into conflict dynamics across diverse zones.},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
This paper introduces a novel framework for conflict analysis that leverages advanced visual modeling techniques. By employing comparative analysis, key variables influencing armed conflicts are identified and analyzed. The framework includes a meta-model representing domain concepts such as the goals and strategies of conflicting parties, escalating stages, and impacts of conflicts. Conflict escalation is a complex process characterized by interactions between opposing parties. This paper presents a structured model that outlines how conflicts evolve and intensify over time. We adapt a meta-modeling framework called the Diagram Predicate Framework (DPF) to represent conflict-related concepts and extend it to support abstract view generation. This framework facilitates the analysis of conflict trends and the study of dynamics across various levels of abstraction. A computational model based on category theory is proposed for trend analysis, enabling the extraction of patterns of conflict evolution and the comparison of strategies and goals at different escalation stages. Categorical operations such as pullback and limit construction are employed to compute conflict evolution and identify common structures among conflict instances, providing insights into conflict dynamics across diverse zones.
2023
Fatemi, Bahareh; Rabbi, Fazle; Opdahl, Andreas L.
Evaluating the Effectiveness of GPT Large Language Model for News Classification in the IPTC News Ontology Journal Article
In: IEEE Access, 2023.
@article{GPTLangMo,
title = {Evaluating the Effectiveness of GPT Large Language Model for News Classification in the IPTC News Ontology},
author = {Bahareh Fatemi and Fazle Rabbi and Andreas L. Opdahl },
url = {https://mediafutures.no/evaluating_the_effectiveness_of_gpt_large_language_model_for_news_classification_in_the_iptc_news_ontology/},
year = {2023},
date = {2023-12-21},
journal = {IEEE Access},
abstract = {News classification plays a vital role in newsrooms, as it involves the time-consuming task
of categorizing news articles and requires domain knowledge. Effective news classification is essential
for categorizing and organizing a constant flow of information, serving as the foundation for subsequent
tasks, such as news aggregation, monitoring, filtering, and organization. The automation of this process can
significantly benefit newsrooms by saving time and resources. In this study, we explore the potential of the
GPT large language model in a zero-shot setting for multi-class classification of news articles within the
widely accepted International Press Telecommunications Council (IPTC) news ontology. The IPTC news
ontology provides a structured framework for categorizing news, facilitating the efficient organization and
retrieval of news content. By investigating the effectiveness of the GPT language model in this classification
task, we aimed to understand its capabilities and potential applications in the news domain. This study was
conducted as part of our ongoing research in the field of automated journalism.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
News classification plays a vital role in newsrooms, as it involves the time-consuming task
of categorizing news articles and requires domain knowledge. Effective news classification is essential
for categorizing and organizing a constant flow of information, serving as the foundation for subsequent
tasks, such as news aggregation, monitoring, filtering, and organization. The automation of this process can
significantly benefit newsrooms by saving time and resources. In this study, we explore the potential of the
GPT large language model in a zero-shot setting for multi-class classification of news articles within the
widely accepted International Press Telecommunications Council (IPTC) news ontology. The IPTC news
ontology provides a structured framework for categorizing news, facilitating the efficient organization and
retrieval of news content. By investigating the effectiveness of the GPT language model in this classification
task, we aimed to understand its capabilities and potential applications in the news domain. This study was
conducted as part of our ongoing research in the field of automated journalism.
of categorizing news articles and requires domain knowledge. Effective news classification is essential
for categorizing and organizing a constant flow of information, serving as the foundation for subsequent
tasks, such as news aggregation, monitoring, filtering, and organization. The automation of this process can
significantly benefit newsrooms by saving time and resources. In this study, we explore the potential of the
GPT large language model in a zero-shot setting for multi-class classification of news articles within the
widely accepted International Press Telecommunications Council (IPTC) news ontology. The IPTC news
ontology provides a structured framework for categorizing news, facilitating the efficient organization and
retrieval of news content. By investigating the effectiveness of the GPT language model in this classification
task, we aimed to understand its capabilities and potential applications in the news domain. This study was
conducted as part of our ongoing research in the field of automated journalism.
Rabbi, Fazle; Fatemi, Bahareh; Lamo, Yngve; Opdahl, Andreas L.
A model-based framework for NEWS content analysis Journal Article
In: 12th International Conference on Model-Based Software and Systems Engineering, 2023.
@article{modelBased23,
title = {A model-based framework for NEWS content analysis},
author = {Fazle Rabbi and Bahareh Fatemi and Yngve Lamo and Andreas L. Opdahl},
url = {https://mediafutures.no/news-content-analysis/},
year = {2023},
date = {2023-12-12},
urldate = {2023-12-12},
journal = {12th International Conference on Model-Based Software and Systems Engineering},
abstract = {News articles are published all over the world to cover important events. Journalists need to keep track of
ongoing events in a fair and accountable manner and analyze them for newsworthiness. It requires enormous
amount of time for journalists to process information coming from main stream news media, social media
from all over the world as well as policy and law circulated by governments and international organizations.
News articles published by different news providers may consist of subjectivity of the reporters due to the
influence of reporters’ backgrounds, world views and opinions. In today’s practice of journalism there is a
lack of computational methods to support journalists to investigate fairness and monitor and analyze large
massive information streams. In this paper we present a model based approach to analyze the perspectives of
news publishers and monitor the progression of news events from various perspective. The domain concepts
in the news domain such as the news events and their contextual information is represented across various
dimensions in a knowledge graph. We presented a multi dimensional comparative analysis method of news
events for analyzing news article variants and for uncovering underlying storylines. To show the applicability
of the proposed method in real life, we demonstrated a running example in this paper. The utilization of
a model-based approach ensures the adaptability of our proposed method for representing a wide array of
domain concepts within the news domain.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
News articles are published all over the world to cover important events. Journalists need to keep track of
ongoing events in a fair and accountable manner and analyze them for newsworthiness. It requires enormous
amount of time for journalists to process information coming from main stream news media, social media
from all over the world as well as policy and law circulated by governments and international organizations.
News articles published by different news providers may consist of subjectivity of the reporters due to the
influence of reporters’ backgrounds, world views and opinions. In today’s practice of journalism there is a
lack of computational methods to support journalists to investigate fairness and monitor and analyze large
massive information streams. In this paper we present a model based approach to analyze the perspectives of
news publishers and monitor the progression of news events from various perspective. The domain concepts
in the news domain such as the news events and their contextual information is represented across various
dimensions in a knowledge graph. We presented a multi dimensional comparative analysis method of news
events for analyzing news article variants and for uncovering underlying storylines. To show the applicability
of the proposed method in real life, we demonstrated a running example in this paper. The utilization of
a model-based approach ensures the adaptability of our proposed method for representing a wide array of
domain concepts within the news domain.
ongoing events in a fair and accountable manner and analyze them for newsworthiness. It requires enormous
amount of time for journalists to process information coming from main stream news media, social media
from all over the world as well as policy and law circulated by governments and international organizations.
News articles published by different news providers may consist of subjectivity of the reporters due to the
influence of reporters’ backgrounds, world views and opinions. In today’s practice of journalism there is a
lack of computational methods to support journalists to investigate fairness and monitor and analyze large
massive information streams. In this paper we present a model based approach to analyze the perspectives of
news publishers and monitor the progression of news events from various perspective. The domain concepts
in the news domain such as the news events and their contextual information is represented across various
dimensions in a knowledge graph. We presented a multi dimensional comparative analysis method of news
events for analyzing news article variants and for uncovering underlying storylines. To show the applicability
of the proposed method in real life, we demonstrated a running example in this paper. The utilization of
a model-based approach ensures the adaptability of our proposed method for representing a wide array of
domain concepts within the news domain.
Fatemi, Bahareh; Rabbi, Fazle; Tessem, Bjørnar
Fairness in automated data journalism systems Journal Article
In: NIKT: Norsk IKT-konferanse for forskning og utdanning, 2023.
@article{nokeyg,
title = {Fairness in automated data journalism systems},
author = {Bahareh Fatemi and Fazle Rabbi and Bjørnar Tessem},
url = {https://www.researchgate.net/publication/365127564_Fairness_in_automated_data_journalism_systems},
doi = {10.13140/RG.2.2.30374.19522},
year = {2023},
date = {2023-03-09},
urldate = {2023-03-09},
journal = {NIKT: Norsk IKT-konferanse for forskning og utdanning},
abstract = {Automated data journalism is an application of computing and artificial intelligence (AI) that aims to create stories from raw data, possibly in a variety of formats (such as visuals or text). Conventionally, a variety of methodologies and tools, including statistical software packages and data visualization tools have been used to generate stories from raw data. Artificial intelligence, and particularly machine learning techniques have recently been introduced because they can handle more complex data and scale more easily to larger datasets. However, AI techniques may raise a number of ethical concerns such as an unfair presentation which typically occurs due to bias. Stories that contains unfair presentation could be destructive at individual and societal levels; they could also damage the reputation of news providers. In this paper we study an existing framework of automated journalism and enhance the framework to make it aware of fairness concern. We present various steps of the framework where bias enters into the production of a story and address the causes and effects of different types of biases.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Automated data journalism is an application of computing and artificial intelligence (AI) that aims to create stories from raw data, possibly in a variety of formats (such as visuals or text). Conventionally, a variety of methodologies and tools, including statistical software packages and data visualization tools have been used to generate stories from raw data. Artificial intelligence, and particularly machine learning techniques have recently been introduced because they can handle more complex data and scale more easily to larger datasets. However, AI techniques may raise a number of ethical concerns such as an unfair presentation which typically occurs due to bias. Stories that contains unfair presentation could be destructive at individual and societal levels; they could also damage the reputation of news providers. In this paper we study an existing framework of automated journalism and enhance the framework to make it aware of fairness concern. We present various steps of the framework where bias enters into the production of a story and address the causes and effects of different types of biases.
Khan, Sohail Ahmed; Sheikhi, Ghazaal; Opdahl, Andreas L.; Rabbi, Fazle; Stoppel, Sergej; Trattner, Christoph; Dang-Nguyen, Duc-Tien
Visual User-Generated Content Verification in Journalism: An Overview Journal Article
In: IEEE Access, 2023.
@article{KHAN2023,
title = {Visual User-Generated Content Verification in Journalism: An Overview},
author = {Sohail Ahmed Khan and Ghazaal Sheikhi and Andreas L. Opdahl and Fazle Rabbi and Sergej Stoppel and Christoph Trattner and Duc-Tien Dang-Nguyen},
url = {https://mediafutures.no/e0ret1-visual_user-generated_content_verification_in_journalism_an_overview/},
year = {2023},
date = {2023-01-16},
urldate = {2023-01-16},
journal = {IEEE Access},
abstract = {Over the past few years, social media has become an indispensable part of the news generation and dissemination cycle on the global stage. These digital channels along with the easy-to-use editing tools have unfortunately created a medium for spreading mis-/disinformation containing visual content. Media practitioners and fact-checkers continue to struggle with scrutinising and debunking visual user-generated content (UGC) quickly and thoroughly as verification of visual content requires a high level of expertise and could be exceedingly complex amid the existing computational tools employed in newsrooms. The aim of this study is to present a forward-looking perspective on how visual UGC verification in journalism can be transformed by multimedia forensics research. We elaborate on a comprehensive overview of the five elements of the UGC verification and propose multimedia forensics as the sixth element. In addition, different types of visual content forgeries and detection approaches proposed by the computer science research community are explained. Finally, a mapping of the available verification tools media practitioners rely on is created along with their limitations and future research directions to gain the confidence of media professionals in using multimedia forensics tools in their day-to-day routine.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Over the past few years, social media has become an indispensable part of the news generation and dissemination cycle on the global stage. These digital channels along with the easy-to-use editing tools have unfortunately created a medium for spreading mis-/disinformation containing visual content. Media practitioners and fact-checkers continue to struggle with scrutinising and debunking visual user-generated content (UGC) quickly and thoroughly as verification of visual content requires a high level of expertise and could be exceedingly complex amid the existing computational tools employed in newsrooms. The aim of this study is to present a forward-looking perspective on how visual UGC verification in journalism can be transformed by multimedia forensics research. We elaborate on a comprehensive overview of the five elements of the UGC verification and propose multimedia forensics as the sixth element. In addition, different types of visual content forgeries and detection approaches proposed by the computer science research community are explained. Finally, a mapping of the available verification tools media practitioners rely on is created along with their limitations and future research directions to gain the confidence of media professionals in using multimedia forensics tools in their day-to-day routine.