• Save
News Article Propagation Analysis on Twitter
Upcoming SlideShare
Loading in...5
×
 

News Article Propagation Analysis on Twitter

on

  • 1,575 views

This is a research study that examines how News Articles propagate on Twitter. It reports on a comparison of 12 major news media agencies using Network, and temporal analysis of a dataset collected ...

This is a research study that examines how News Articles propagate on Twitter. It reports on a comparison of 12 major news media agencies using Network, and temporal analysis of a dataset collected from Twitter.

Statistics

Views

Total Views
1,575
Views on SlideShare
1,501
Embed Views
74

Actions

Likes
2
Downloads
0
Comments
0

2 Embeds 74

http://www.scoop.it 70
http://localhost 4

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

News Article Propagation Analysis on Twitter News Article Propagation Analysis on Twitter Presentation Transcript

  • Sharing News Articles Using 140 Characters: A Propagation Analysis on Twitter Dr. Sudha Ram McClelland Professor of MIS and Computer Science Eller College of Management University of Arizona Tucson, AZ 86721 Email: ram@eller.arizona.edu April 2, 2013 INSITE: Center for Business Intelligence and Analytics www.insiteua.org This is joint work with Devi Bhattacharya Friday, April 05, 2013
  • MotivationTwitter – Important hub of Social Media  140 million members1  340 million tweets/day1Evolution of Twitter as a serious newswire - Credible news appearing on it before anywhere else on the web2  Iran election results controversy  Egyptian revolution 1 - as of March, 2012 via http://blog.twitter.com/2012/03/twitter-turns-six.html 2 - http://blog.twitter.com/2007/09/twitter-for-news.html, http://blog.twitter.com/2008/07/twitter-as-news-wire.html, http://www.sysomos.com/insidetwitter/engagement/Friday, April 05, 2013
  • Research Objective “Understanding and comparing the influence of news agency brands in a micro-blogging environment using network analysis” Measuring the news article cascades caused by Twitter users’ participation Investigating Twitter news agency Followers’ involvement in the propagation process Theory development for model Identifying and producing of online news article new network measures propagation4/5/2013 4:34 AM
  • Our Initial Questions How do different news sources compare in volume and extent of spread of news articles? How do the articles from different news sources spread and survive over time and what is their lifespan? How do rates of spread compare across different news sources?Friday, April 05, 2013
  • Data CollectionDataset of tweets (21st Nov till 13th Dec, 2011)  Tweet containing valid URL of articles from selected news sources.  Excludes tweets containing a URL that refers to the news source homepage  URL Minimum reach 5 or more (tweets,re- tweeted/posted)  6 Million tweetsVia Twitter Streaming API and Phirehose applicationFriday, April 05, 2013
  • Twitter Propagation Network A weighted user-user network : G = (V, E, W) s v1 v2 where,  V : the set of nodes, representing the users on Twitter who tweet/retweet about a news media article  E : the set of edges, signifying two users who are linked via tweet-retweet/reply relationship  W : the normalized edge weight representing the strength of the tweet-retweet/reply relationship. (Strength s = Number of times v2 retweets/replies v1) This network is named as Twitter Activity Network (TAN) Captures the user participation in news propagation for the entire time period4/5/2013 4:34 AM
  • Example of Twitter Activity Network NYTimes TAN for 1 week activity #Nodes : 58,945 #Edges : 29,6764/5/2013 4:34 AM EGO
  • Example of TAN: WaPo Washington Post Non-media seeding node cascade streams Disconnected network section – single user nodes capturing tweets with no responseFriday, April 05, 2013
  • QUESTION 1 How do different news sources compare in volume and extent of spread of news articles?  Nodes volume and edge/node ratio comparison using TAN  Ego network node analysisFriday, April 05, 2013
  • Diffusion Network Statistics News Source Media Seeding Nodes Edges Diameter Edge/Node Nodes RatioBBC bbcworld, 3801581 154276 341 0.406 Higher number of Lowest bbcbreaking, Bloomberg, NPR, Washington long cascade diameter High NYTimes Vs. bbcnews Post, Forbes, and Wired : High edge/node streamsReuters reuters Sparse # 61440 27035 non-media nodes 1 Washington Post retweets/replies – % of 8 0.44 ratioGuardian guardiannews 21454 9250creating cascades 3 0.4312 High concentration ofNYTimes as information Acting nytimes 1556503 individual tweets63436 143 0.4084NPR boosters for news Financial each nprnews 40454 16459 4 0.407 other agenciesWashington Post washingtonpost 982474 40791 networks three major seedingLow BBC 152 0.4153 edge/node nodes – bbcworld, bbcbreaking andFT ft 10242 2925 4 long Fewer bbcnews 0.286 ratios Information spreadForbes concentrated at levels 88630 forbes 28718 9 cascade 0.324Bloomberg closerbloombergnews 26487 to the seeding 8719 streams. 104 0.33Arstechnica node arstechnica 13313 5016 3 0.377Mashable mashable 1971092 78620 8 0.398Wired wired 43744 14786 9 0.338 Superscript values indicate rank for that columnFriday, April 05, 2013
  • Example of Twitter Ego Network NYTimes Ego Network for 1 week activity #Nodes : 22,176 #Edges : 23,081 Ego Network ⊆ TAN4/5/2013 4:34 AM TAN
  • Analyzing News Cascades – Diffusion Depth Day-wise Comparison of Average Diffusion Depth 8 BBC News Guardian – M NYTimes – 7 – 5.43 4.00 2.14 Tu Diffusion Depth 6 Washington FT – 1.71 Post – 4.00 W 5 Arstechnica – 2.29 Th 4 3 F 2 Sa 1 Su 0 Avg Lowest Average Highest Average Weekly Diffusion Weekly Diffusion Depth Depth 4/5/2013 4:34 AM
  • QUESTION 2 How do the articles from different news sources spread and survive over time and what is their lifespan? Analyzing Lifespan: Time difference between the last and first tweet posted containing the URL to an article Examining article survival distributionsFriday, April 05, 2013
  • Life Span of Articles on Twitter Shortest Lifespan Longest Lifespan NYTimes Bloomberg Wired Forbes Mashable BBC • Y-Axis: Count of articles surviving • X-Axis: Time Progression from first tweet submission time (In Hours)Friday, April 05, 2013
  • QUESTION 3 How do rates of spread compare across different news sources analyzed in comparison with tweet posting activity?  Concept of URL half-life  Cumulative and non-cumulative trendsFriday, April 05, 2013
  • Cumulative Number of Tweets Posted News Sources Exceeding Forbes BBC Half-Life - Fair News Sources Exceeding Half-Life - Mashable Most Popular Washington Wired NYTimes Reuters Post • Y-Axis: Cumulative number of tweets posted for a given article • X-Axis: Time Progression from first tweet submission time (In Hours) • Tweets Frequency restricted between 0-400 and Time Progression to 72 hoursFriday, April 05, 2013
  • Summary of Findings• BBC – Maximum reach in terms of affected users, retweet levels and distribution of Twitter users in tweet-retweet ladders. – Best article survival chances, with 0.1% of articles surviving for three days or more – Median lifespan of article is 55 hours from first submission. – Majorly supported by 2 other BBC Twitter accounts – bbcbreaking and bbcworld.• Reuters – Average article lifespan and rate of spread – Best edge/node ratio - indicating highest percentage of user interactions via retweets and replies• NYTimes and Mashable – Similar tweets volume and rate of spread – Mashable - high percentage of level 1 tweets and NYTimes - tweet-retweet cascades resulting in a diameter of 14 levels.• Guardian – Most of the tweets in its ego network are concentrated in the first level – Second highest edge/node ratio. – Highest median number of tweets posted in the first hour – Third highest rate of spread Friday, April 05, 2013
  • Applying the network measures for news agency comparison  Considering the collective influence of the network measures on news agency article propagation Diffusion depth and Diffusion magnitude appear to be inversely related Follower engagement seems directly News Agencies with Involvementproportional to of followers high tweeting activity is more important magnitude Diffusion than the number diffusion have high of followers depth 4/5/2013 4:34 AM
  • Implications Exhaustive analysis of news media agency related tweets Leads for developing diffusion strategy to maximize audience reach  Magnitude Vs. Depth Identification of useful metrics to evaluate propagation Diffusion Follower Participation, Lifespan and Diameter User Volume related metrics Edge/Node Node composition at Cumulative and Ratio, Reach different levels of ego non-cumulative rate network of spreadFriday, April 05, 2013
  • NPR VideoClick here to view the NPR news item onour work:http://www.insiteua.org/news/Twitter_Becomes_News_Media_Tool.asp4/5/2013 20
  • Publication ReferenceFor more information please see ourresearch paper:Devipsita Bhattacharya and Sudha Ram, “SharingNews Article Using 140 Characters: A DiffusionAnalysis on Twitter”, IEEE 2012 InternationalConference on Advances in Social Network Analysisand Mining (ASONAM 2012), pp, 966-971.http://www.computer.org/csdl/proceedings/asonam/2012/4799/00/4799a966-abs.html4/5/2013 21
  • Harnessing the Power of Social Media Using INSITE Facebook ID: BusinessIntelligenceAndAnalyticsCenter Twitter ID: insite_ua4/5/2013 4:34 AM
  • Questions INSITE: Center for Business Intelligence and Analytics URL: www.insiteua.org Contact Information: ram@eller.arizona.edu4/5/2013 23