"In a digital era the oldest Russian news agency ""TASS"" employs BigData and ML to analyze the wide scope of news data published on the Internet in order to monitor a media space, measure the performance of the editorial, compare it to competitors, and enhance its market presence. We use ML techniques such as text distance, PageRank, clustering, graph representation, coupled with BigData approaches to undertsand the amount of citations - and Citation Index - of the TASS news by other media companies, and use these insights to model topics in news on a daily basis. In this talk I will show technology and application of a topic modelling approach based on the major Russian news data, with implications to business value for the big news agency."

1. NEWS TOPIC
MODELLING BASED ON
CITATION DETECTION
ALEXEY BURNAKOV

2. Contents
• Introducing TASS
• Digitalization
• Our products
• Technological stack
• NLP pipeline
• Data science methods
• Data insights

3. TASS: Information Telegraph Agency of Russia
Largest Russian News Agency
Founded in 1904
www.tass.com

4. TASS: History
115th birthday
It's been a while…

5. Digitalization
Digitalization is the use of digital technologies to change
a business model and provide new revenue and value-producing
opportunities…
www.gartner.com
How can one build a data driven culture in such
an organization? WE EXPERIMENT!

6. Our Products. Topic Modelling. News topic saturation

7. Our Products. Citation Detection. Cite index distribution
Personal
data
Headline Editorial
Cite
number
Cite
index
Citing
media
DateCite index distribution

8. Our Products. Citation Index
Cite index dynamics

9. Our Products. Sentiment of Mass Media

10. Our Stack
MVP Prod

11. NLP Pipeline
Raw text Tokenization Who cites
TASS
Which
editorial’s
cited
Topic
coverage
Customer
facing

12. Methods
Cosine similarity PageRank

13. Citation Detection: math
𝑝 =
1
1 + 𝑒−(𝑏0 𝐶𝑜𝑠𝑖𝑛𝑒 +𝑏1 𝑇𝑖𝑚𝑒 +𝑏2 )
We use cosine distance between news,
combined with the publish time delta between the news,
fitted via logistic regression to the probability of citation
on a labeled dataset.

14. Citation Detection: results
precision = 0.89
recall = 0.87
Using pure cosine distance Using log-reg model output
F1 score = 0.88
MCC score = 0.88
AUC: 0.988 AUC: 0.998

15. PageRank: math
The PageRank values are the entries of the dominant
right eigenvector of the modified adjacency matrix
rescaled so that each column adds up to one

16. PageRank: results. TOP-25 of the Russian Mass Media

17. Data Insights

18. Topic Modelling I
What are the big-deal
topics for today?
Notre-Dame de
Paris’s on fire : (

19. Topic Modelling II
What are the big-deal
topics for today?
Ukrainian President
elections day
Blasts in
Shri-Lanka :(

20. Topic Modelling III
What are the big-deal
topics for today?
Flood in the Irkutsk
Region :(
Submarine deadly
accident :(

21. Daily Competition Snapshot
News agency Time lag < 1H Time lag > 1H Absent in topic Topic number News number
Citation
number
Citation index
ТАСС 53% 5% 42% 89 135 267 59
РИА Новости 45% 1% 54% 71 125 222 44
Интерфакс 18% 3% 79% 32 54 86 16
Topic ratnig News agency Citation index News number
Citation
number
RIA 11 40 77
TASS 9 30 64
Interfax 4 14 35
RIA 5 6 13
TASS 2 3 15
Interfax 1 2 6
TASS 4 5 12
Interfax 1 2 3
RIA 0,5 4 6
TASS 3 2 5
RIA 1 2 5
Interfax 1 1 1
TASS 2 2 5
RIA 1,5 1 7
Interfax 0,1 2 5
1
2
3
4
5

22. Topic
rating
Publish
time
Headline
Mass Media
Follow-up
News Citations in Topics
Notre-Dame de
Paris’s on fire

23. Author Performance in Topics
Notre-Dame de Paris’s on fire
Topic
rating
Author Editorial board
Citation
index
Citation
number

24. Editorial Performance by Citation Index TOP-10
Editorial board
Citation
number
Citation
index
Share