US Election Web Monitor

The US Election 2020 Web Monitor is a freely available Web intelligence platform to analyze the public debate and track the candidates’ performance on the campaign trail.

www.weblyzard.com/us-election-2020-web-monitor

Its Elasticsearch-powered visual analytics dashboard is an advanced information exploration system to automatically identify opinion leaders, and to classify online coverage along multiple metadata dimensions - e.g., by topic or geographic location. The system is continuously updated with live content feeds, reflecting social perceptions and events that impact the coverage.

Transcript

1. 1
   US Election 2020
   Web Monitor
   us2020.weblyzard.com
   Arno Scharl, Alexander Hubmann-Haidvogel
   webLyzard technology ▪ Modul University Vienna
   www.weblyzard.com/us-election-2020-web-monitor
   

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3. 3
   Sentiment
   Share of Voice Brand Reputation
   Measuring Social Perceptions
   Success Metrics
   WYSDOM
   Success Metric
   Wheel of Emotions
   

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4. 4
   2004
   Scharl, A. and Weichselbraun, A. (2008). “An Automated Approach to
   Investigating the Online Media Coverage of US Presidential Elections”,
   Journal of Information Technology & Politics, 5(1): 121-132.
   

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13. 13 Elasticsearch | Integration
    History
    - Started with PostgreSQL/Tsearch2
    - Switched to Apache Lucene in 2009
    - Adopted Elasticsearch early 2014
    Current Cluster
    - 5 (soon 7) physical machines – XEON E5, 40 cores, 256GB RAM
    - 3 x 2TB M.2 NVMe Samsung 960 Pro in striped LVM
    - Multiple Elasticsearch nodes per machine
    4 data nodes, separate master nodes,
    one coordinating-only node per machine
    - Docker containers, overlay network, discovery using DNS
    

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14. 14 Elasticsearch | Integration
    Indexing
    – Custom component based on Vert.x
    – Data read from CockroachDB (PostgreSQL for legacy data)
    – Indexer applies transformations/de-normalizations
    – Enriched with additional metadata
    e.g., translations, named entities, geographic coordinates
    – One index per source, language and month
    e.g., English-language news media: en.4.media.2021-01
    – Balance between index/shard size and number of indexes
    affected by queries and aggregations
    

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15. 15 Elasticsearch | Visualization
    Example 1: Geographic Map
    – Hashgrid aggregation for extracted locations
    – User-selectable precision
    – Aggregate average document sentiment
    Example 2: Word Tree
    – Inner hits on search query
    – Filtered for sentences matching the query
    – Maintaining document-level sorting
    

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16. 16 Elasticsearch | Visualization
    Example 3: Story Clustering
    – Aggregate top keywords per interval in date histogram
    – Sub-aggregation of top associations for each keyword
    – Converted to graph (connect keywords in subsequent
    timespans)
    – Community detection using Louvain algorithm
    – Date histogram aggregation for each story to obtain
    document count over time
    

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17. 17
    Sentiment
    Share of Voice Brand Reputation
    Measuring Social Perceptions
    Success Metrics
    WYSDOM
    Success Metric
    Wheel of Emotions
    Weichselbraun, A., Steixner, J., Braşovean, A., Scharl, A., Göbel, M. and Nixon, L. (2021).
    “Automatic Expansion of Domain-Specific Affective Models for Web Intelligence Applications”,
    Cognitive Computation: Forthcoming.
    Weichselbraun, A., Gindl, S., Fischer, F., Vakulenko, S. and Scharl, A. (2017).
    “Aspect-Based Extraction and Analysis of Affective Knowledge from Social Media Streams”,
    IEEE Intelligent Systems, 32(3): 80-88.
    

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18. 18
    Sentiment
    Share of Voice Brand Reputation
    WYSDOM
    Success Metric
    Wheel of Emotions
    Measuring Social Perceptions
    Success Metrics
    

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19. 19
    Innovation R&D Funding – Selected Research Projects
    

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20. 20 Newsletter
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    Technology Showcases
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    info@weblyzard.com
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    weblyzard-technology
    

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