BroadcastSTAND: Clustering
Multimedia Sources of News
Jason Zhang, Ai-Te Kuo, Nicole R. Schneider,
Jacob Peters, Hanan SametIntroduction

The modern media landscape involves diverse
channels, including newspapers, social media, radio,and TV

Overview of the NewsStand architecture, which
traditionally focuses on online news articles andTwitter posts

Introduction of BroadcastSTAND as an extension tointegrate radio and TV broadcasts into the clusteringlandscapeObjectives andSignificanceObjective: Evaluate the viability of incorporating
broadcast news into the NewsStand framework

Emphasis on the importance of gaining insights fromdiverse news sources for a more comprehensive
understanding of current events

NewsStandArchitecture

Original NewsStand architecture focuses on online
news articles and Twitter posts

Utilizes techniques such as content analysis,clustering, and recommendation algorithms for news
organization and retrieval

NewsStand demo:
https://player.vimeo.com/video/106352925
NewsStand system:
https://newsstand.umiacs.umd.edu/web/

BroadcastSTAND
Framework:
A multimedia
approach to news

BroadcastSTAND is an extension to NewsStand

Includes radio and TV broadcast transcripts tobroaden the scope of news content

Growing significance of broadcast data, including
podcasts and YouTube channels

Enriching the user experience with diverse news
perspectives and audiovisual content

Method andClustering Analysis
Results

Use of PBS NewsHour transcripts for analysis

Identification of three types of clusters: BroadcastOnly, All type, and Non-Broadcast

Unexpected clustering outcome for broadcast news
transcripts

Introduction of BroadcastStand as a response toaddress clustering challenges associated withbroadcast data

Broadcast Media Clusters Mostly Separatelyfrom Other News Documents
Radio-only clusters
Non-radio clusters
All type clusters

T-SNE visualization of high dimensional cluster space projected into 2 dimensions

Evaluation Metrics

Precision and recall metrics evaluation

The average recall for all the keywords was only
28.46%, while the average precision for all clusters
was 99.74%

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