SlideShare a Scribd company logo
1 of 14
Download to read offline
Detecting subtle text manipulations
A cross-article analysis chasing the signals of media framing
Date: 25 March 2020
Author: Martino Mensio
1
Information disorder
https://firstdraftnews.org/latest/fake-news-complicated/ 2
Media framing and bias
• Selection of details (Agenda setting [1]):
– Omitting details
– Rescale importance
• Specific word choices
• Mix of factual and subjective
• Argument distortion
3[1] Cohen, B.C., 2015. Press and foreign policy. Princeton University Press.
The need for cross-article analysis
Acknowledge: framing cannot be avoided
Research questions:
• Can we help the reader being aware of the framing
around the piece of news that is being consumed?
• Can we enrich the study of media framing with a
comparative analysis?
4
Two disjoint areas of research
5
+ Document clustering (e.g., news aggregators)
+ Corroboration and omissions of information [3]
+ Plagiarism detection
+ Semantic frames [2]
+ Structural role [4]
+ Sentiment and subjectivity [5]
- Analysis of differences is left to the reader - One article at a time
Document relationships Media framing analysis
Cross-article framing analysis [7]
+ Main focus change
+ Ordering
+ Selection of details
+ Framing differences
[2] Charles J Fillmore. Frame semantics. Cognitive linguistics: Basic readings, 34:373–400, 2006.
[3] Bountouridis, D., Marrero, M., Tintarev, N. and Hauff, C., 2018. Explaining credibility in news articles using cross-referencing. In SIGIR
workshop on ExplainAble Recommendation and Search (EARS).
[4] Zahid, I., Zhang, H., Boons, F. and Batista-Navarro, R., 2019. Towards the Automatic Analysis of the Structure of News Stories. In Text2Story@
ECIR (pp. 71-79).
[5] Liu, B., 2010. Sentiment analysis and subjectivity. Handbook of natural language processing, 2(2010), pp.627-666.
[6] Mensio, M.; Alani, H. and Willis, A., 2020. Towards a Cross-article Narrative Comparison of News. In: Proceedings of the Text2Story’20
Workshop, CEUR WS.
http://oro.open.ac.uk/69887/
Similarity
6
Similarity - Objective
Similarity applied at different levels:
• Article: same events (e.g., News aggregators)
• Sentence: the same detail (e.g. [3])
• Word: find specific words
Resistant to:
• Changes in the linguistic surface
• Changes in framing
7
[3] Bountouridis, D., Marrero, M., Tintarev, N. and Hauff, C., 2018. Explaining credibility in news articles using cross-referencing. In SIGIR
workshop on ExplainAble Recommendation and Search (EARS).
Similarity applied
Documents
Document
vectors
Documents
adjacency
matrix
Documents
Graph
[7] Devlin, J., Chang, M.W., Lee, K. and Toutanova, K., 2018. Bert: Pre-training of deep bidirectional transformers for language understanding.
arXiv preprint arXiv:1810.04805.
[8] Cer, D., Yang, Y., Kong, S.Y., Hua, N., Limtiaco, N., John, R.S., Constant, N., Guajardo-Cespedes, M., Yuan, S., Tar, C. and Sung, Y.H., 2018.
Universal sentence encoder. arXiv preprint arXiv:1803.11175.
[9] Johnson, J., Douze, M. and Jégou, H., 2019. Billion-scale similarity search with GPUs. IEEE Transactions on Big Data. 8
Sentences Sentence
vectors
Sentences
adjacency
matrix
Sentences
Graph
Sentencisation
•similarity
•Indexing [9]
•Threshold
•Cliques
Embedding [7,8]
Example: sentence-sentence similarity
First article: https://www.bbc.co.uk/news/uk-northern-ireland-51478855
Second article: https://news.sky.com/story/lyra-mckee-man-charged-with-murder-of-journalist-in-northern-ireland-11932429
9
Example: word-word similarity
First article: https://www.bbc.co.uk/news/uk-england-hereford-worcester-51791346
Second article: https://www.dailymail.co.uk/news/article-8088805/Britons-facing-heavy-downpours-four-inches-rain-50mph-winds-set-batter-UK.html
10
Example: word-word similarity
1: “Coronavirus: Strict new curbs on life in UK announced by PM” https://www.bbc.co.uk/news/uk-52012432
2: “The moment a British prime minister put the whole nation under house arrest”
https://www.independent.co.uk/voices/lockdown-coronavirus-boris-johnson-address-statement-social-distancing-isolate-a9420131.html
• Shopping trips should be as infrequent as possible • One form of exercise a day such as a run,
walk, or cycle.
• We are allowed out to buy necessities – “as infrequently as possible” – and to do our state-
approved exercise – once a day.
11
Media Framing Signals
12
Framing indicators
• Semantic frames [2]
• Structural role [4]
• Subjectivity / sentiment strength [5]
13
[2] Charles J Fillmore. Frame semantics. Cognitive linguistics: Basic readings, 34:373–400, 2006.
[4] Zahid, I., Zhang, H., Boons, F. and Batista-Navarro, R., 2019. Towards the Automatic Analysis of the Structure of News Stories. In Text2Story@
ECIR (pp. 71-79).
[5] Liu, B., 2010. Sentiment analysis and subjectivity. Handbook of natural language processing, 2(2010), pp.627-666.
That’s all for now…
https://twitter.com/MartinoMensio
14
Similarity
Framing
signals
Evaluation

More Related Content

Similar to Detecting subtle text manipulations

Quantum Criticism: an Analysis of Political News Reporting
Quantum Criticism: an Analysis of Political News ReportingQuantum Criticism: an Analysis of Political News Reporting
Quantum Criticism: an Analysis of Political News Reportingmlaij
 
IRJET- Fake News Detection
IRJET- Fake News DetectionIRJET- Fake News Detection
IRJET- Fake News DetectionIRJET Journal
 
Putting News in a Perspective: Framing by Word Choice and Labeling
Putting News in a Perspective: Framing by Word Choice and LabelingPutting News in a Perspective: Framing by Word Choice and Labeling
Putting News in a Perspective: Framing by Word Choice and LabelingAnastasia Zhukova
 
Book Title In Essay.pdf
Book Title In Essay.pdfBook Title In Essay.pdf
Book Title In Essay.pdfAndrea Warner
 
Interpretable Topic Modeling Using Near-Identity Cross-Document Coreference R...
Interpretable Topic Modeling Using Near-Identity Cross-Document Coreference R...Interpretable Topic Modeling Using Near-Identity Cross-Document Coreference R...
Interpretable Topic Modeling Using Near-Identity Cross-Document Coreference R...Anastasia Zhukova
 
Interpreting the Semantics of Anomalies Based on Mutual Information in Link M...
Interpreting the Semantics of Anomalies Based on Mutual Information in Link M...Interpreting the Semantics of Anomalies Based on Mutual Information in Link M...
Interpreting the Semantics of Anomalies Based on Mutual Information in Link M...ijdms
 
Do you ever use facebook
Do you ever use facebookDo you ever use facebook
Do you ever use facebookhgfhjjff
 
Scientific Reproducibility from an Informatics Perspective
Scientific Reproducibility from an Informatics PerspectiveScientific Reproducibility from an Informatics Perspective
Scientific Reproducibility from an Informatics PerspectiveMicah Altman
 
Reproducibility from an infomatics perspective
Reproducibility from an infomatics perspectiveReproducibility from an infomatics perspective
Reproducibility from an infomatics perspectiveMicah Altman
 
Vakulenko PhD Status Report - 16 February 2016
Vakulenko PhD Status Report - 16 February 2016Vakulenko PhD Status Report - 16 February 2016
Vakulenko PhD Status Report - 16 February 2016Svitlana Vakulenko
 
BROWN BAG TALK WITH MICAH ALTMAN, SOURCES OF BIG DATA FOR SOCIAL SCIENCES
BROWN BAG TALK WITH MICAH ALTMAN, SOURCES OF BIG DATA FOR SOCIAL SCIENCESBROWN BAG TALK WITH MICAH ALTMAN, SOURCES OF BIG DATA FOR SOCIAL SCIENCES
BROWN BAG TALK WITH MICAH ALTMAN, SOURCES OF BIG DATA FOR SOCIAL SCIENCESMicah Altman
 
Topic Modeling : Clustering of Deep Webpages
Topic Modeling : Clustering of Deep WebpagesTopic Modeling : Clustering of Deep Webpages
Topic Modeling : Clustering of Deep Webpagescsandit
 
Topic Modeling : Clustering of Deep Webpages
Topic Modeling : Clustering of Deep WebpagesTopic Modeling : Clustering of Deep Webpages
Topic Modeling : Clustering of Deep Webpagescsandit
 
How CNN's iReport Works the Research
How CNN's iReport Works the ResearchHow CNN's iReport Works the Research
How CNN's iReport Works the ResearchAmani Channel
 
Tracking Social Media Participation: New Approaches to Studying User-Gener...
Tracking  Social  Media  Participation: New Approaches to Studying User-Gener...Tracking  Social  Media  Participation: New Approaches to Studying User-Gener...
Tracking Social Media Participation: New Approaches to Studying User-Gener...Axel Bruns
 
Max newlands social media_ altmetrics_journalism and the future of academic ...
Max newlands  social media_ altmetrics_journalism and the future of academic ...Max newlands  social media_ altmetrics_journalism and the future of academic ...
Max newlands social media_ altmetrics_journalism and the future of academic ...maxnewlands
 

Similar to Detecting subtle text manipulations (20)

Quantum Criticism: an Analysis of Political News Reporting
Quantum Criticism: an Analysis of Political News ReportingQuantum Criticism: an Analysis of Political News Reporting
Quantum Criticism: an Analysis of Political News Reporting
 
IRJET- Fake News Detection
IRJET- Fake News DetectionIRJET- Fake News Detection
IRJET- Fake News Detection
 
Putting News in a Perspective: Framing by Word Choice and Labeling
Putting News in a Perspective: Framing by Word Choice and LabelingPutting News in a Perspective: Framing by Word Choice and Labeling
Putting News in a Perspective: Framing by Word Choice and Labeling
 
IJET-V3I2P23
IJET-V3I2P23IJET-V3I2P23
IJET-V3I2P23
 
Book Title In Essay.pdf
Book Title In Essay.pdfBook Title In Essay.pdf
Book Title In Essay.pdf
 
A Tale of Two Cities
A Tale of Two CitiesA Tale of Two Cities
A Tale of Two Cities
 
Interpretable Topic Modeling Using Near-Identity Cross-Document Coreference R...
Interpretable Topic Modeling Using Near-Identity Cross-Document Coreference R...Interpretable Topic Modeling Using Near-Identity Cross-Document Coreference R...
Interpretable Topic Modeling Using Near-Identity Cross-Document Coreference R...
 
Interpreting the Semantics of Anomalies Based on Mutual Information in Link M...
Interpreting the Semantics of Anomalies Based on Mutual Information in Link M...Interpreting the Semantics of Anomalies Based on Mutual Information in Link M...
Interpreting the Semantics of Anomalies Based on Mutual Information in Link M...
 
Mike thelwall ritu
Mike thelwall rituMike thelwall ritu
Mike thelwall ritu
 
20320130406021 2
20320130406021 220320130406021 2
20320130406021 2
 
Do you ever use facebook
Do you ever use facebookDo you ever use facebook
Do you ever use facebook
 
Scientific Reproducibility from an Informatics Perspective
Scientific Reproducibility from an Informatics PerspectiveScientific Reproducibility from an Informatics Perspective
Scientific Reproducibility from an Informatics Perspective
 
Reproducibility from an infomatics perspective
Reproducibility from an infomatics perspectiveReproducibility from an infomatics perspective
Reproducibility from an infomatics perspective
 
Vakulenko PhD Status Report - 16 February 2016
Vakulenko PhD Status Report - 16 February 2016Vakulenko PhD Status Report - 16 February 2016
Vakulenko PhD Status Report - 16 February 2016
 
BROWN BAG TALK WITH MICAH ALTMAN, SOURCES OF BIG DATA FOR SOCIAL SCIENCES
BROWN BAG TALK WITH MICAH ALTMAN, SOURCES OF BIG DATA FOR SOCIAL SCIENCESBROWN BAG TALK WITH MICAH ALTMAN, SOURCES OF BIG DATA FOR SOCIAL SCIENCES
BROWN BAG TALK WITH MICAH ALTMAN, SOURCES OF BIG DATA FOR SOCIAL SCIENCES
 
Topic Modeling : Clustering of Deep Webpages
Topic Modeling : Clustering of Deep WebpagesTopic Modeling : Clustering of Deep Webpages
Topic Modeling : Clustering of Deep Webpages
 
Topic Modeling : Clustering of Deep Webpages
Topic Modeling : Clustering of Deep WebpagesTopic Modeling : Clustering of Deep Webpages
Topic Modeling : Clustering of Deep Webpages
 
How CNN's iReport Works the Research
How CNN's iReport Works the ResearchHow CNN's iReport Works the Research
How CNN's iReport Works the Research
 
Tracking Social Media Participation: New Approaches to Studying User-Gener...
Tracking  Social  Media  Participation: New Approaches to Studying User-Gener...Tracking  Social  Media  Participation: New Approaches to Studying User-Gener...
Tracking Social Media Participation: New Approaches to Studying User-Gener...
 
Max newlands social media_ altmetrics_journalism and the future of academic ...
Max newlands  social media_ altmetrics_journalism and the future of academic ...Max newlands  social media_ altmetrics_journalism and the future of academic ...
Max newlands social media_ altmetrics_journalism and the future of academic ...
 

More from Martino Mensio

Towards a Cross-Article Narrative Comparison of News
Towards a Cross-Article Narrative Comparison of NewsTowards a Cross-Article Narrative Comparison of News
Towards a Cross-Article Narrative Comparison of NewsMartino Mensio
 
News Source Credibility in the Eyes of Different Assessors
News Source Credibility in the Eyes of Different AssessorsNews Source Credibility in the Eyes of Different Assessors
News Source Credibility in the Eyes of Different AssessorsMartino Mensio
 
A Multi-layer LSTM-based Approach for Robot Command Interaction Modeling
A Multi-layer LSTM-based Approach for Robot Command Interaction ModelingA Multi-layer LSTM-based Approach for Robot Command Interaction Modeling
A Multi-layer LSTM-based Approach for Robot Command Interaction ModelingMartino Mensio
 
The Rise of Emotion-aware Conversational Agents: Threats in Digital Emotions
The Rise of Emotion-aware Conversational Agents: Threats in Digital EmotionsThe Rise of Emotion-aware Conversational Agents: Threats in Digital Emotions
The Rise of Emotion-aware Conversational Agents: Threats in Digital EmotionsMartino Mensio
 
Multi-turn QA: A RNN Contextual Approach to Intent Classification for Goal-or...
Multi-turn QA: A RNN Contextual Approach to Intent Classification for Goal-or...Multi-turn QA: A RNN Contextual Approach to Intent Classification for Goal-or...
Multi-turn QA: A RNN Contextual Approach to Intent Classification for Goal-or...Martino Mensio
 
Deep Semantic Learning for Conversational Agents
Deep Semantic Learning for Conversational AgentsDeep Semantic Learning for Conversational Agents
Deep Semantic Learning for Conversational AgentsMartino Mensio
 
Deep Learning per la Comprensione del Linguaggio Naturale - HKN
Deep Learning per la Comprensione del Linguaggio Naturale - HKNDeep Learning per la Comprensione del Linguaggio Naturale - HKN
Deep Learning per la Comprensione del Linguaggio Naturale - HKNMartino Mensio
 

More from Martino Mensio (7)

Towards a Cross-Article Narrative Comparison of News
Towards a Cross-Article Narrative Comparison of NewsTowards a Cross-Article Narrative Comparison of News
Towards a Cross-Article Narrative Comparison of News
 
News Source Credibility in the Eyes of Different Assessors
News Source Credibility in the Eyes of Different AssessorsNews Source Credibility in the Eyes of Different Assessors
News Source Credibility in the Eyes of Different Assessors
 
A Multi-layer LSTM-based Approach for Robot Command Interaction Modeling
A Multi-layer LSTM-based Approach for Robot Command Interaction ModelingA Multi-layer LSTM-based Approach for Robot Command Interaction Modeling
A Multi-layer LSTM-based Approach for Robot Command Interaction Modeling
 
The Rise of Emotion-aware Conversational Agents: Threats in Digital Emotions
The Rise of Emotion-aware Conversational Agents: Threats in Digital EmotionsThe Rise of Emotion-aware Conversational Agents: Threats in Digital Emotions
The Rise of Emotion-aware Conversational Agents: Threats in Digital Emotions
 
Multi-turn QA: A RNN Contextual Approach to Intent Classification for Goal-or...
Multi-turn QA: A RNN Contextual Approach to Intent Classification for Goal-or...Multi-turn QA: A RNN Contextual Approach to Intent Classification for Goal-or...
Multi-turn QA: A RNN Contextual Approach to Intent Classification for Goal-or...
 
Deep Semantic Learning for Conversational Agents
Deep Semantic Learning for Conversational AgentsDeep Semantic Learning for Conversational Agents
Deep Semantic Learning for Conversational Agents
 
Deep Learning per la Comprensione del Linguaggio Naturale - HKN
Deep Learning per la Comprensione del Linguaggio Naturale - HKNDeep Learning per la Comprensione del Linguaggio Naturale - HKN
Deep Learning per la Comprensione del Linguaggio Naturale - HKN
 

Recently uploaded

Mature dropshipping via API with DroFx.pptx
Mature dropshipping via API with DroFx.pptxMature dropshipping via API with DroFx.pptx
Mature dropshipping via API with DroFx.pptxolyaivanovalion
 
B2 Creative Industry Response Evaluation.docx
B2 Creative Industry Response Evaluation.docxB2 Creative Industry Response Evaluation.docx
B2 Creative Industry Response Evaluation.docxStephen266013
 
Carero dropshipping via API with DroFx.pptx
Carero dropshipping via API with DroFx.pptxCarero dropshipping via API with DroFx.pptx
Carero dropshipping via API with DroFx.pptxolyaivanovalion
 
VIP High Profile Call Girls Amravati Aarushi 8250192130 Independent Escort Se...
VIP High Profile Call Girls Amravati Aarushi 8250192130 Independent Escort Se...VIP High Profile Call Girls Amravati Aarushi 8250192130 Independent Escort Se...
VIP High Profile Call Girls Amravati Aarushi 8250192130 Independent Escort Se...Suhani Kapoor
 
Introduction-to-Machine-Learning (1).pptx
Introduction-to-Machine-Learning (1).pptxIntroduction-to-Machine-Learning (1).pptx
Introduction-to-Machine-Learning (1).pptxfirstjob4
 
Generative AI on Enterprise Cloud with NiFi and Milvus
Generative AI on Enterprise Cloud with NiFi and MilvusGenerative AI on Enterprise Cloud with NiFi and Milvus
Generative AI on Enterprise Cloud with NiFi and MilvusTimothy Spann
 
CebaBaby dropshipping via API with DroFX.pptx
CebaBaby dropshipping via API with DroFX.pptxCebaBaby dropshipping via API with DroFX.pptx
CebaBaby dropshipping via API with DroFX.pptxolyaivanovalion
 
Low Rate Call Girls Bhilai Anika 8250192130 Independent Escort Service Bhilai
Low Rate Call Girls Bhilai Anika 8250192130 Independent Escort Service BhilaiLow Rate Call Girls Bhilai Anika 8250192130 Independent Escort Service Bhilai
Low Rate Call Girls Bhilai Anika 8250192130 Independent Escort Service BhilaiSuhani Kapoor
 
꧁❤ Greater Noida Call Girls Delhi ❤꧂ 9711199171 ☎️ Hard And Sexy Vip Call
꧁❤ Greater Noida Call Girls Delhi ❤꧂ 9711199171 ☎️ Hard And Sexy Vip Call꧁❤ Greater Noida Call Girls Delhi ❤꧂ 9711199171 ☎️ Hard And Sexy Vip Call
꧁❤ Greater Noida Call Girls Delhi ❤꧂ 9711199171 ☎️ Hard And Sexy Vip Callshivangimorya083
 
Delhi Call Girls CP 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call
Delhi Call Girls CP 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip CallDelhi Call Girls CP 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call
Delhi Call Girls CP 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Callshivangimorya083
 
Midocean dropshipping via API with DroFx
Midocean dropshipping via API with DroFxMidocean dropshipping via API with DroFx
Midocean dropshipping via API with DroFxolyaivanovalion
 
VIP High Class Call Girls Jamshedpur Anushka 8250192130 Independent Escort Se...
VIP High Class Call Girls Jamshedpur Anushka 8250192130 Independent Escort Se...VIP High Class Call Girls Jamshedpur Anushka 8250192130 Independent Escort Se...
VIP High Class Call Girls Jamshedpur Anushka 8250192130 Independent Escort Se...Suhani Kapoor
 
Smarteg dropshipping via API with DroFx.pptx
Smarteg dropshipping via API with DroFx.pptxSmarteg dropshipping via API with DroFx.pptx
Smarteg dropshipping via API with DroFx.pptxolyaivanovalion
 
Brighton SEO | April 2024 | Data Storytelling
Brighton SEO | April 2024 | Data StorytellingBrighton SEO | April 2024 | Data Storytelling
Brighton SEO | April 2024 | Data StorytellingNeil Barnes
 
Call me @ 9892124323 Cheap Rate Call Girls in Vashi with Real Photo 100% Secure
Call me @ 9892124323  Cheap Rate Call Girls in Vashi with Real Photo 100% SecureCall me @ 9892124323  Cheap Rate Call Girls in Vashi with Real Photo 100% Secure
Call me @ 9892124323 Cheap Rate Call Girls in Vashi with Real Photo 100% SecurePooja Nehwal
 
BabyOno dropshipping via API with DroFx.pptx
BabyOno dropshipping via API with DroFx.pptxBabyOno dropshipping via API with DroFx.pptx
BabyOno dropshipping via API with DroFx.pptxolyaivanovalion
 
FESE Capital Markets Fact Sheet 2024 Q1.pdf
FESE Capital Markets Fact Sheet 2024 Q1.pdfFESE Capital Markets Fact Sheet 2024 Q1.pdf
FESE Capital Markets Fact Sheet 2024 Q1.pdfMarinCaroMartnezBerg
 
100-Concepts-of-AI by Anupama Kate .pptx
100-Concepts-of-AI by Anupama Kate .pptx100-Concepts-of-AI by Anupama Kate .pptx
100-Concepts-of-AI by Anupama Kate .pptxAnupama Kate
 
Data-Analysis for Chicago Crime Data 2023
Data-Analysis for Chicago Crime Data  2023Data-Analysis for Chicago Crime Data  2023
Data-Analysis for Chicago Crime Data 2023ymrp368
 

Recently uploaded (20)

Mature dropshipping via API with DroFx.pptx
Mature dropshipping via API with DroFx.pptxMature dropshipping via API with DroFx.pptx
Mature dropshipping via API with DroFx.pptx
 
B2 Creative Industry Response Evaluation.docx
B2 Creative Industry Response Evaluation.docxB2 Creative Industry Response Evaluation.docx
B2 Creative Industry Response Evaluation.docx
 
Carero dropshipping via API with DroFx.pptx
Carero dropshipping via API with DroFx.pptxCarero dropshipping via API with DroFx.pptx
Carero dropshipping via API with DroFx.pptx
 
VIP High Profile Call Girls Amravati Aarushi 8250192130 Independent Escort Se...
VIP High Profile Call Girls Amravati Aarushi 8250192130 Independent Escort Se...VIP High Profile Call Girls Amravati Aarushi 8250192130 Independent Escort Se...
VIP High Profile Call Girls Amravati Aarushi 8250192130 Independent Escort Se...
 
Introduction-to-Machine-Learning (1).pptx
Introduction-to-Machine-Learning (1).pptxIntroduction-to-Machine-Learning (1).pptx
Introduction-to-Machine-Learning (1).pptx
 
Generative AI on Enterprise Cloud with NiFi and Milvus
Generative AI on Enterprise Cloud with NiFi and MilvusGenerative AI on Enterprise Cloud with NiFi and Milvus
Generative AI on Enterprise Cloud with NiFi and Milvus
 
CebaBaby dropshipping via API with DroFX.pptx
CebaBaby dropshipping via API with DroFX.pptxCebaBaby dropshipping via API with DroFX.pptx
CebaBaby dropshipping via API with DroFX.pptx
 
Low Rate Call Girls Bhilai Anika 8250192130 Independent Escort Service Bhilai
Low Rate Call Girls Bhilai Anika 8250192130 Independent Escort Service BhilaiLow Rate Call Girls Bhilai Anika 8250192130 Independent Escort Service Bhilai
Low Rate Call Girls Bhilai Anika 8250192130 Independent Escort Service Bhilai
 
Sampling (random) method and Non random.ppt
Sampling (random) method and Non random.pptSampling (random) method and Non random.ppt
Sampling (random) method and Non random.ppt
 
꧁❤ Greater Noida Call Girls Delhi ❤꧂ 9711199171 ☎️ Hard And Sexy Vip Call
꧁❤ Greater Noida Call Girls Delhi ❤꧂ 9711199171 ☎️ Hard And Sexy Vip Call꧁❤ Greater Noida Call Girls Delhi ❤꧂ 9711199171 ☎️ Hard And Sexy Vip Call
꧁❤ Greater Noida Call Girls Delhi ❤꧂ 9711199171 ☎️ Hard And Sexy Vip Call
 
Delhi Call Girls CP 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call
Delhi Call Girls CP 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip CallDelhi Call Girls CP 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call
Delhi Call Girls CP 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call
 
Midocean dropshipping via API with DroFx
Midocean dropshipping via API with DroFxMidocean dropshipping via API with DroFx
Midocean dropshipping via API with DroFx
 
VIP High Class Call Girls Jamshedpur Anushka 8250192130 Independent Escort Se...
VIP High Class Call Girls Jamshedpur Anushka 8250192130 Independent Escort Se...VIP High Class Call Girls Jamshedpur Anushka 8250192130 Independent Escort Se...
VIP High Class Call Girls Jamshedpur Anushka 8250192130 Independent Escort Se...
 
Smarteg dropshipping via API with DroFx.pptx
Smarteg dropshipping via API with DroFx.pptxSmarteg dropshipping via API with DroFx.pptx
Smarteg dropshipping via API with DroFx.pptx
 
Brighton SEO | April 2024 | Data Storytelling
Brighton SEO | April 2024 | Data StorytellingBrighton SEO | April 2024 | Data Storytelling
Brighton SEO | April 2024 | Data Storytelling
 
Call me @ 9892124323 Cheap Rate Call Girls in Vashi with Real Photo 100% Secure
Call me @ 9892124323  Cheap Rate Call Girls in Vashi with Real Photo 100% SecureCall me @ 9892124323  Cheap Rate Call Girls in Vashi with Real Photo 100% Secure
Call me @ 9892124323 Cheap Rate Call Girls in Vashi with Real Photo 100% Secure
 
BabyOno dropshipping via API with DroFx.pptx
BabyOno dropshipping via API with DroFx.pptxBabyOno dropshipping via API with DroFx.pptx
BabyOno dropshipping via API with DroFx.pptx
 
FESE Capital Markets Fact Sheet 2024 Q1.pdf
FESE Capital Markets Fact Sheet 2024 Q1.pdfFESE Capital Markets Fact Sheet 2024 Q1.pdf
FESE Capital Markets Fact Sheet 2024 Q1.pdf
 
100-Concepts-of-AI by Anupama Kate .pptx
100-Concepts-of-AI by Anupama Kate .pptx100-Concepts-of-AI by Anupama Kate .pptx
100-Concepts-of-AI by Anupama Kate .pptx
 
Data-Analysis for Chicago Crime Data 2023
Data-Analysis for Chicago Crime Data  2023Data-Analysis for Chicago Crime Data  2023
Data-Analysis for Chicago Crime Data 2023
 

Detecting subtle text manipulations

  • 1. Detecting subtle text manipulations A cross-article analysis chasing the signals of media framing Date: 25 March 2020 Author: Martino Mensio 1
  • 3. Media framing and bias • Selection of details (Agenda setting [1]): – Omitting details – Rescale importance • Specific word choices • Mix of factual and subjective • Argument distortion 3[1] Cohen, B.C., 2015. Press and foreign policy. Princeton University Press.
  • 4. The need for cross-article analysis Acknowledge: framing cannot be avoided Research questions: • Can we help the reader being aware of the framing around the piece of news that is being consumed? • Can we enrich the study of media framing with a comparative analysis? 4
  • 5. Two disjoint areas of research 5 + Document clustering (e.g., news aggregators) + Corroboration and omissions of information [3] + Plagiarism detection + Semantic frames [2] + Structural role [4] + Sentiment and subjectivity [5] - Analysis of differences is left to the reader - One article at a time Document relationships Media framing analysis Cross-article framing analysis [7] + Main focus change + Ordering + Selection of details + Framing differences [2] Charles J Fillmore. Frame semantics. Cognitive linguistics: Basic readings, 34:373–400, 2006. [3] Bountouridis, D., Marrero, M., Tintarev, N. and Hauff, C., 2018. Explaining credibility in news articles using cross-referencing. In SIGIR workshop on ExplainAble Recommendation and Search (EARS). [4] Zahid, I., Zhang, H., Boons, F. and Batista-Navarro, R., 2019. Towards the Automatic Analysis of the Structure of News Stories. In Text2Story@ ECIR (pp. 71-79). [5] Liu, B., 2010. Sentiment analysis and subjectivity. Handbook of natural language processing, 2(2010), pp.627-666. [6] Mensio, M.; Alani, H. and Willis, A., 2020. Towards a Cross-article Narrative Comparison of News. In: Proceedings of the Text2Story’20 Workshop, CEUR WS. http://oro.open.ac.uk/69887/
  • 7. Similarity - Objective Similarity applied at different levels: • Article: same events (e.g., News aggregators) • Sentence: the same detail (e.g. [3]) • Word: find specific words Resistant to: • Changes in the linguistic surface • Changes in framing 7 [3] Bountouridis, D., Marrero, M., Tintarev, N. and Hauff, C., 2018. Explaining credibility in news articles using cross-referencing. In SIGIR workshop on ExplainAble Recommendation and Search (EARS).
  • 8. Similarity applied Documents Document vectors Documents adjacency matrix Documents Graph [7] Devlin, J., Chang, M.W., Lee, K. and Toutanova, K., 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. [8] Cer, D., Yang, Y., Kong, S.Y., Hua, N., Limtiaco, N., John, R.S., Constant, N., Guajardo-Cespedes, M., Yuan, S., Tar, C. and Sung, Y.H., 2018. Universal sentence encoder. arXiv preprint arXiv:1803.11175. [9] Johnson, J., Douze, M. and Jégou, H., 2019. Billion-scale similarity search with GPUs. IEEE Transactions on Big Data. 8 Sentences Sentence vectors Sentences adjacency matrix Sentences Graph Sentencisation •similarity •Indexing [9] •Threshold •Cliques Embedding [7,8]
  • 9. Example: sentence-sentence similarity First article: https://www.bbc.co.uk/news/uk-northern-ireland-51478855 Second article: https://news.sky.com/story/lyra-mckee-man-charged-with-murder-of-journalist-in-northern-ireland-11932429 9
  • 10. Example: word-word similarity First article: https://www.bbc.co.uk/news/uk-england-hereford-worcester-51791346 Second article: https://www.dailymail.co.uk/news/article-8088805/Britons-facing-heavy-downpours-four-inches-rain-50mph-winds-set-batter-UK.html 10
  • 11. Example: word-word similarity 1: “Coronavirus: Strict new curbs on life in UK announced by PM” https://www.bbc.co.uk/news/uk-52012432 2: “The moment a British prime minister put the whole nation under house arrest” https://www.independent.co.uk/voices/lockdown-coronavirus-boris-johnson-address-statement-social-distancing-isolate-a9420131.html • Shopping trips should be as infrequent as possible • One form of exercise a day such as a run, walk, or cycle. • We are allowed out to buy necessities – “as infrequently as possible” – and to do our state- approved exercise – once a day. 11
  • 13. Framing indicators • Semantic frames [2] • Structural role [4] • Subjectivity / sentiment strength [5] 13 [2] Charles J Fillmore. Frame semantics. Cognitive linguistics: Basic readings, 34:373–400, 2006. [4] Zahid, I., Zhang, H., Boons, F. and Batista-Navarro, R., 2019. Towards the Automatic Analysis of the Structure of News Stories. In Text2Story@ ECIR (pp. 71-79). [5] Liu, B., 2010. Sentiment analysis and subjectivity. Handbook of natural language processing, 2(2010), pp.627-666.
  • 14. That’s all for now… https://twitter.com/MartinoMensio 14 Similarity Framing signals Evaluation