Emotions, Experiences and Social Media Maarten de Rijke University - - PowerPoint PPT Presentation

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Emotions, Experiences and Social Media Maarten de Rijke University - - PowerPoint PPT Presentation

Aug 18, 2023 276 likes •492 views

Emotions, Experiences and Social Media Maarten de Rijke University of Amsterdam 1 Another perspective Standards, infrastructure as seen by an academic research group Intelligent information access Content-based matching

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Emotions, Experiences and Social Media

Maarten de Rijke

University of Amsterdam

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Another perspective

  • Standards, infrastructure as seen by an academic

research group

  • Intelligent information access
  • Content-based matching
  • Additional features (recency, authoritativeness, novelty, opinionatedness, …)
  • Combine content-based and additional features
  • Presentation

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Research strategy

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Theory Experiment Application

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Online lives

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Why?

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What?

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What?

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A web of applications

Social media analysis Learning from implicit feedback Reputation management: identifying and tracking stakeholders Finding experiences to inform creation of new products Real-time impact prediction Multilingual log file analysis Interest machine Email search Computational humanities and social sciences Linking archives Providing access to all parliamentary data in Europe News archives meet video archives meet … Open data initiatives Religious studies Medical anthropology Communication science Governmental Chronobiology E-discovery Detecting radicalization Entity mining Enrichment through linking Aggregated entity search

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Political Mashup

  • Aggregating parliamentary

data

  • Debates, debate structure
  • “Semantification”
  • Linking to video broadcasts,

twitter, blogs, party programs

  • Tracking topic ownership

from parliament to social media and back

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CoSyne

  • Translate between wiki

pages

  • Identify changes in
  • ne page
  • Find gaps in other,

target pages

  • Translate material to

be inserted in gaps

  • Insert translated

material in gaps

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  • Mood annotated blogs
  • Real-time mood tracking and prediction
  • MoodViews (2005-2009)
  • Moodgrapher: follow
  • Moodteller: predict
  • Moodsignals: explain
  • Moodspotter: discover associations
  • Analyzing ‘old’ data: chronobiology

The mood of the web

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  • Mood annotated blogs
  • Real-time mood tracking and prediction
  • MoodViews (2005-2009)
  • Moodgrapher: follow
  • Moodteller: predict
  • Moodsignals: explain
  • Moodspotter: discover associations
  • Analyzing ‘old’ data: chronobiology

The mood of the web

http://www.moodviews.com

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  • Mood annotated blogs
  • Real-time mood tracking and prediction
  • MoodViews (2005-2009)
  • Moodgrapher: follow
  • Moodteller: predict
  • Moodsignals: explain
  • Moodspotter: discover associations
  • Analyzing ‘old’ data: chronobiology

The mood of the web

http://www.moodviews.com

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  • Mood annotated blogs
  • Real-time mood tracking and prediction
  • MoodViews (2005-2009)
  • Moodgrapher: follow
  • Moodteller: predict
  • Moodsignals: explain
  • Moodspotter: discover associations
  • Analyzing ‘old’ data: chronobiology

The mood of the web

http://www.moodviews.com

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  • Mood annotated blogs
  • Real-time mood tracking and prediction
  • MoodViews (2005-2009)
  • Moodgrapher: follow
  • Moodteller: predict
  • Moodsignals: explain
  • Moodspotter: discover associations
  • Analyzing ‘old’ data: chronobiology

The mood of the web

http://www.moodviews.com

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  • Mood annotated blogs
  • Real-time mood tracking and prediction
  • MoodViews (2005-2009)
  • Moodgrapher: follow
  • Moodteller: predict
  • Moodsignals: explain
  • Moodspotter: discover associations
  • Analyzing ‘old’ data: chronobiology

The mood of the web

http://www.moodviews.com

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06/03/05 08/01/05 10/01/05 12/01/05 02/01/06 04/01/06 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

Original data versus Trend

ratio of blog posts labeled with STRESSED

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Ingredients

  • Search engine technologies
  • Content extraction
  • Language technologies
  • Semistructured data technologies
  • Scaleable distributed processing

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  • We are scientists, developers, users at the

same time and we have external partners

  • Agile vs standards?
  • Let a 1000 flowers bloom?

Development strategy

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vs.

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Fietstas

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Fietstas Programmer API Python Sscrape Fietstas web scraper for RSS feeds or sites WWW Fietstas Fietstas worker Stemming, NEN, NER, term cloud aggregation, ... Fietstas Web API XMLRPC or REST Fietstas Inspector Inspect document annotations direct document upload file by file or in batches Fietstas worker Stemming, NEN, NER, term cloud aggregation, ... Fietstas worker Stemming, NEN, NER, term cloud aggregation, ...

documents

Text analysis service (NL, EN)

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  • A look from the lab
  • Social media as a “societal thermometer”
  • Many opportunities for public-private

collaborations

  • Infrastructure for supporting these

collaborations

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  • Based on joint work with
  • Krisztian Balog, Wouter Bolsterlee,

Breyten Ernsting, Valentin Jijkoun, Fons Laan, Maarten Marx, Gilad Mishne, Christof Monz, Daan Odijk, Ork de Rooij, Manos Tsagkias, Andrei Vishneuski

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