Yngve Lamo
Researcher
Høgskulen på Vestlandet
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.
2023
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.