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Bringing Citizens’ Opinions to Members of Parliament
The Newspaper Story
Ruxandra Geana Steve Taylor
Timo WandhoeferCeDEM2012, Donau Universität Krems
Contents
• Background & Context• Problem Statement• Solution Strategy• Results• Conclusions & Further Work
Background
Context: WeGov Project
• Allow policy makers to interact directly with citizens using Social Network Sites– Use the tools the citizens already use
• Find and understandpeople’s opinions
• Become part of the discussion
• Open dialog• Respect privacy• Encourage trust
WeGov Toolbox
Search
Analysis Analysis
Scenario
Scenario
• Policy makers want to test policy statements with citizens
• Direct contact on SNS can be inefficient• Often policy statements are issued as press releases
– Newspaper articles written about them– These are discussed across social media– We need to find where the comments are made on
policy statements• We can then use other tools in the toolbox to
collect postings and provide further analysis
Scenario
Research Questions & Strategy to Answer Them
Research Questions & Answers
1. How can we find out where a news article is being discussed?
2. How can we track the locations for 1) over time?
3. How can we track a news story containing many news articles?
4. How can we find news articles related to a press release or an MP’s statement?
5. Which are the important places a policy maker needs to monitor for a particular subject area?
1. Google for the headline and URL of the article
2. Schedule repeated automatic searches and store ranked results
3. Group searches into stories
4. Search specific newspaper sites for press release
5. Group searches into subject areas & record top tens
Search Strategy
• Google searches for news article headline & URL return sites that reference our news article
• Google is by far the most popular search engine in the Western world– therefore it is very likely to be used by many people
who may want to comment on a news article• Google’s ranking is important
– it scores pages by relevance and the number of citations– therefore popular pages that reference our news article
will be ranked highly
Store ranked search results
in database
InternetInternet
Scheduler
Display & Data Analysis
Google Custom SearchDatabase
Data Selection
URL + Headline
User
News Article Search
Basic ComponentsFind news
articles
Search Google for the articles’ URL & headline
Configure Scheduler to
repeat search at defined intervals
Date: ???Results:
Headline Search
URL Search
www L1www G2www M3www J4www A5...
www99www100
www J1www K2www F3www D4www E5...
www99www100
Story, Press Release, Minister’s Statement
...
time
Aggregated Results: Day n
www AA1www J2
www AP3www AE4www AW5
...www99www100
www AC6www K7www L8
www AM9www10
Aggregated Results: Day 3
www A
1www J2www Q3www P4
www W
5
...www99www100
www X6
www K
7 www S8
www M9www10
Aggregated Results: Day 2
www A1 www J2
www Q3
www B
4www W5
...www99www100
www X6 www K7
www L8
www M
9www10
Aggregated Results: Day 1
www A1www J2www Q3www B4www W5
...www99www100
www X6www K7www L8www M9
www10
Analyse results
News Article
...
Date: 7 June 2011Results:
Headline Search
URL Search
www B1www A2www C3www G4www5
...www99www100
www D1www E2www F3www4www5
Date: 9 June 2011Results:
Headline Search
URL Search
www G1www H2www B3www A4www5
...www99www100
www F1www D2www E3www4www5
...www99www100
Date: 11 June 2011Results:
Headline Search
URL Search
www G1www J2www B3www A4www5
...www99www100
www F1www J2www K3www D4www E5...
www99www100
Date: ???Results:
Headline Search
URL Search
www L1www G2www M3www J4www A5...
www99www100
www J1www K2www F3www D4www E5...
www99www100
time
Date: 5 June 2011Results:
Headline Search
URL Search
www A1www B2www C3www4www5
...www99www100
www D1www E2www F3www4
Illustration of Searches Over Time
• Sites referencing the news article change over time• “Pop Charts” of Rankings
– sites rise & fall, new entrants
Analysis: Selection & Aggregation
• Data can be analysed in different ways• Select a subset of data
– e.g news story or subject area, each with many articles
• how do the locations change over time?• what is the top location over all times?
• Other examples:– select all news stories in a subject area– select all news stories in one week– select news stories from one publisher
News Stories, Articles & OLAP
• One news story– Multiple news
articles (z axis)– Multiple searches
over time per article (x axis)
• We can “collapse” x and z axes to get averages for all values of that feature
Averaging
• Bayesian average chosen to average chart positions– Takes account of number of samples each web
site has– The more instances a site has, the more sure we
are that its position is accurate• Avoids spurious end-members
Illustration
Main UINews
Headlines Scheduled search results
Most Popular Domains Over All SearchesLower
average is better
Can blacklist
sites
Top Domains for Selected Searches
Select a News Story and a time
frame
Popular Domains Changing Each Week
Conclusions
• A governmental policy maker can discover where comments are made on policy statements– Often news articles are written about policy statements and the
articles are discussed over the internet• We use scheduled and repeated Google searches for news
articles and collect the results in a database– We can aggregate analyse search results to produce ranked
tables of sites that reference each news article• Using data mining techniques such as the OLAP cube, we
can group data in many ways– We can examine aggregate scores taking into account multiple
datasets, averaging out individual differences
