Arcomem training Twitter Domain Experts advanced

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This presentation on Twitter Domain Experts is part of the ARCOMEM training curriculum. Feel free to roam around or contact us on Twitter via @arcomem to learn more about ARCOMEM training on archiving Social Media.

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Arcomem training Twitter Domain Experts advanced

  1. 1. 1 Twitter Domain Expert Detection
  2. 2. 2 Overview •Tweets cover most topics of interest •Many experts from different domains use twitter to express their opinions or talk about new findings in their field of interest •Domain Experts provide access to: –New topics and trends –High quality content via Links –Comments on current events ARCOMEM, Domain Experts Training material
  3. 3. 3 Detecting Domain Experts •Workflow for finding Domain Experts 1.Tweets are enriched with Wikipedia Articles 2.Articles are related to Wikipedia categories 3.The categorie graph is used to define top categories 4.Aggregation of categoies mentioned by a user builds the userprofile 5.Domain Experts are identified by their user profile ARCOMEM, Domain Experts Training material
  4. 4. 4 Annotation - Wikipedia Miner Wikipedia-Miner is used for annotating the Tweets with links to Wikipedia articles and based on two main steps •Disambiguation •Link detection •Booth steps are based on machine-learning algorithms. The disambiguation step uses the context in which words appear to find the corresponding article. •Link detection is based on several features and tries to create a link structure like in WikipediaARCOMEM, Domain Experts Training material
  5. 5. 5 Articles and Categories Articles are linked to corresponding categories •Wikipedia Category Graph is used to generate relations between articles and top-level categories •Weight depends on siblings and distance ARCOMEM, Domain Experts Training material
  6. 6. 6 ARCOMEM, Domain Experts Training material Building a User Profile
  7. 7. 7 ARCOMEM, Domain Experts Training material Architecture
  8. 8. 8 ARCOMEM, Domain Experts Training material Implementation Details •Written in JAVA •NER is based on Wikipedia-Minier •Wikipedia-Miner API is used for parsing categorie graph •Profiles are stored in Knowledge Base
  9. 9. 9 ARCOMEM, Domain Experts Training material Evolution of Domain Experts •Detected Experts can change over time •Current Experts can help finding new ones (looking at retweets, friends, mentions) •System learns who is an Expert by looking at: –Content of tweets –Social Graph –Groups
  10. 10. 10 ARCOMEM, Domain Experts Training material UI for Exploring Domain Experts •Interface for users will provide a view on the domain expert profiles and tweets –Who are the experts for a certain domain –Topics they tweet about –What is their level of expertice
  11. 11. 11 ARCOMEM, Domain Experts Training material Demo http://twikime.l3s.uni-hannover.de/twikime.php
  12. 12. 12 ARCOMEM, Domain Experts Training material Results •Domain Expert knowledge will be used for the Online Analysis –Prioritization Module as feature for priorization –Which users shall be crawled –Detect Users which help getting into the topic •Extracted tweets and URLS will be displayed to users together with Domain Expert profiles

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