Social insect inspired approach for

identification and dynamic tracking

of news stories on the Web

Štefan Sabo and Pavol Návrat

sabo@fiit.stuba.sk, navrat@fiit.stuba.sk

General overview

• Method for dynamic identification and tracking of currently

unfolding news stories is proposed.

• Multiple agents inspired by honey bees foraging for food

are used.

• Connections between articles are explored one story word

at a time, most promising story words that provide links

between articles are propagated.

• Graph of connection between articles and story words is

constructed and analyzed in order to obtain distinct news

stories.

Motivation

• News stories are often represented by terms that identify

the story by providing an easily recognizable label for it.

• These story words are interesting for navigation in the

space of news stories.

• Dynamic system is needed to follow new articles and

account for the changes in the old ones.

• Corpus of all the articles in unavailable.

Method overview – story word extraction

• Story word is a term related to a group of articles, which

connects the articles on a basis of their mutual relevance

to a single news story.

• Instead of identifying stories relevant to news articles

directly, we try to identify individual story words relevant to

an article.

• Advantage of this approach lies in the fact, that in order to

compare two articles based on a single story word, no

global analysis is needed.

• Stories are result of an emergent behavior of the swarm.

Method overview – story word extraction

• Agents move between articles and try to establish most

prominent story words in explored set of articles.

• If a story word is identified based on its relevance to

multiple articles a link is established between the articles.

• Comparison between every two articles regarding every

keyword would be impractical, therefore a selection

strategy based on behavior of honey bees is utilized.

• Modifications, allowing for continuous run have been

introduced.

Method overview – story identification

• Main contribution of presented approach.

• Utilizes a graph representation of obtained data, with

articles and story words as nodes and relevance

relationships as edges.

Method overview – story identification

• Graph is dynamically constructed by agents, as new

articles are explored.

• Articles are grouped into stories through Louvain algorithm

for community detection.

• Once the stories have been established, it is possible to

classify new articles on-the-fly by adding the new articles

to the topics which maximize the modularity of the graph.

• It is however necessary to rerun story identification

periodically from the start, so that the story set is updated

as well.

Results

• Goal to evaluate the precision of topic identification

mechanism.

• Experimental run of 50 agents on Reuters web page for

2000 iterations.

• After obtaining the articles, 11 news stories have been

manually identified and labeled, based on the article

content.

• Afterwards the Louvain algorithm was used to identify

communities within the graph resulting from the

experimental run.

story story words articles Precision [%]

Boston bombing 18 64 96.88

Investor news 4 16 93.75

Cyprus debt 10 56 89.29

Egypt clashes 2 9 88.89

Bank of Japan 10 26 88.46

Italian elections 11 37 83.78

Debt crisis 5 24 83.33

North Korea tension 13 71 74.65

Iraqi elections 4 12 58.33

U.S. gun control 11 49 55.10

Iranian earthquake 12 37 18.92

Total 100 401 75.56

Results – story identification precision

Summary

• Proposed approach utilizes agents to extract story related terms from a set of news articles.

• Articles are compared and their proximity is evaluated multiple times with regard to various story words, using a selection strategy based on Beehive Metaphor.

• Dynamic nature of the process enables agents to react to new articles as well as to changes in the old ones without need for article corpus or machine learning.

• Stories are identified on-the-fly, based on the communities identified within article graph.

• No prior training of agents, or static corpus is required.

Thank you for you attention