In the world of public misinformation, there are many cases where the information is not false or fabricated, but rather has been manipulated using more subtle techniques such as word replacements, selection of details, omissions and argument distortion. These techniques can have the effect of influencing the reader’s frame of mind towards the events reported. We currently lack the necessary tools to uncover such manipulations automatically. In this position paper, we propose an integrated analysis framework and pipeline to identify various narrative signals in news articles; such as structural roles, framing, and subjectivity. By comparing these at the document level and sentence level, it will be possible to highlight differences of narrative techniques used to report the same news events.

1. Towards a Cross-article Narrative Comparison of News
14 April 2020
Martino Mensio, Harith Alani, Alistair Willis Text2Story 2020

2. Inside a News Article
2
Events
• Facts
Narrative
• Storytelling and structure
• Non-chronological order
Framing
• Selection of details
• Word choices
• Mix of facts and opinions

3. Example: beyond the facts
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- Not everyone understands what lockdown means
- Coronavirus Act left uncertainties
- What are your rights?
- Why have there been disagreements?
- Possible actions from police?
- Right to appeal?
- Is there public consent for police powers?
- Guidance delivers reasonable force to police
- Examples of police actions
- Flexible guidance
- Other examples
What are your rights in coronavirus
lockdown Britain?
Lockdown police are told they can use force on
CHILDREN who go outside - and fine parents £60
for failing to stop them
https://www.theguardian.com/world/2020/apr/01/what-are-your-rights-in-
lockdown-britain-coronavirus
https://www.dailymail.co.uk/news/article-8177697/Police-use-reasonable-
force-children-suspected-flouting-coronavirus-lockdown-rules.html
- Loaded language
- Appeal to emotions
- Neutral language
- “Know your rights”

4. The need for cross-article analysis
Research Questions:
• How can we develop tools to reveal the
framing and narrative differences?
• Which cross-article signals can we define
to express the differences?
4

5. Two disjoint areas of research
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+ Document clustering (e.g., news aggregators)
+ Corroboration and omissions of information (Bountouridiset al.)
+ Plagiarism detection
+ Structural role (Zahid et al.)
+ Semantic frames (Fillmore)
+ Subjectivity
- Analysis of differences is left to the reader - One article at a time
Document relationships Narrative and framing analysis
Cross-article analysis
+ Different main focus
+ Different ordering
+ Different selection of details
+ Different framing and subjectivity

6. Similarity
6

7. Similarity - Objective
Similarity applied at different levels:
• Article: aggregate on the same events (e.g., News aggregators)
• Sentence: identify the same detail
• Word: find specific words
Resistant to:
• Changes in the linguistic surface
• Changes in framing
à Candidates: BERT, XLNet, USE
7

8. Example: linguistic surface variation
First article (vertical): BBC
https://www.bbc.co.uk/news/uk-52012432
Second article (horizontal): The Independent
https://www.independent.co.uk/voices/lockdown-coronavirus-boris-johnson-address-statement-social-distancing-isolate-a9420131.html 8

9. Similarity applied
Documents
Document
vectors
Documents
adjacency
matrix
Cross-article
Graph
9
Sentences Sentence
vectors
Sentences
adjacency
matrix
Sentencisation
• Similarity
• Indexing
Linking
• Documents and sentences
• Narrative features
• Embedding
• Narrative features

10. Cross-article signals
10

11. Main focus
Title as proxy of the main focus
11
- Not everyone understands what lockdown means
- Coronavirus Act left uncertainties
- What are your rights?
- Why have there been disagreements?
- Possible actions from police?
- Right to appeal?
- Is there public consent for police powers?
- Guidance delivers reasonable force to police
- Examples of police actions
- Flexible guidance
- Other examples
What are your rights in coronavirus
lockdown Britain?
Lockdown police are told they can use force on
CHILDREN who go outside - and fine parents £60
for failing to stop them
https://www.theguardian.com/world/2020/apr/01/what-are-your-rights-in-
lockdown-britain-coronavirus
https://www.dailymail.co.uk/news/article-8177697/Police-use-reasonable-
force-children-suspected-flouting-coronavirus-lockdown-rules.html

12. Ordering
Similarity links crossovers
12
- Not everyone understands what lockdown means
- Coronavirus Act left uncertainties
- What are your rights?
- Why have there been disagreements?
- Possible actions from police?
- Right to appeal?
- Is there public consent for police powers?
- Guidance delivers reasonable force to police
- Examples of police actions
- Flexible guidance
- Other examples
What are your rights in coronavirus
lockdown Britain?
Lockdown police are told they can use force on
CHILDREN who go outside - and fine parents £60
for failing to stop them
https://www.theguardian.com/world/2020/apr/01/what-are-your-rights-in-
lockdown-britain-coronavirus
https://www.dailymail.co.uk/news/article-8177697/Police-use-reasonable-
force-children-suspected-flouting-coronavirus-lockdown-rules.html

13. Selection of details
First article (vertical): BBC
https://www.bbc.co.uk/news/uk-england-hereford-worcester-51791346
Second article (horizontal): Daily Mail
https://www.dailymail.co.uk/news/article-8088805/Britons-facing-heavy-downpours-four-inches-rain-50mph-winds-set-batter-UK.html
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14. Framing and subjectivity
• Features:
– Semantic frames (Fillmore)
– Subjectivity and sentiment scores
– Usage of word choices
• Why?
– Acknowledge the framing
– Expose to different framing and different subjectivity
– Distinguish facts from opinions
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15. Conclusions
- Framing has big effects
- Comparing different articles provides a broader picture
- We need a tool to do this comparison
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16. References
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).
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.
Cohen, B.C., 2015. Press and foreign policy. Princeton University Press.
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.
Fillmore, C.. Frame semantics. Cognitive linguistics: Basic readings, 34:373–400, 2006.
Johnson, J., Douze, M. and Jégou, H., 2019. Billion-scale similarity search with GPUs. IEEE Transactions on Big Data.
Liu, B., 2010. Sentiment analysis and subjectivity. Handbook of natural language processing, 2(2010), pp.627-666.
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).
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