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Bill Slawski
Interested in seo, search engines, searchers' behaviors, the future of search, the semantic web, music, the environment, and how the Web works.
Interested in seo, search engines, searchers' behaviors, the future of search, the semantic web, music, the environment, and how the Web works.

Bill's posts

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Good to hear Google has solved this problem.

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Is Google Caffeine still important and something you should learn about? Gary Illyes recently tweeted about it as if it were still behind Google's indexing, so I started looking at it some more...

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Google introducing Rich Snippet results for Podcasts to search results.
Google introduces rich results for podcasts

A new data type has appeared in the Google reference library of structured data types: podcasts.

The introductory paragraph for this data type is a pretty good summary of what these results look like, and their current limitations:

You can enable your podcast to appear in Google Search results along with individual episode descriptions and an embedded player for each. This feature applies only to searches in the Google Search app v6.5 or higher on an Android device or on Google Home. We hope to add support for Chrome on Android soon.

Uniquely for recently-added rich results in Google this feature does not rely on, but instead leverages RSS feeds.

Previously Google had extended RSS 2.0 "to define additional elements and attributes" so that publishers could add their podcasts to the Google Play Music Podcast Portal. Those feed requirements can be found here:

The podcast data type further extends this RSS to include iTunes by the use of an itunes: prefix to relevant elements (the feed example declares not only the ../play-podcasts/1.0 namespace, but the iTunes ../podcast-1.0.dtd namespace as well).

In other words, and much more simply, this data type supports podcasts that are distributed either Google Play or iTunes (as well as those published only on individual websites, if I'm reading the requirements correctly). :)

A search feature sure to be embraced by podcasters! And interesting, too, that this is integrated into Google Home (

#podcasts #googleplay #itunes #rss

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Join us for a conversation about Search and SEO.  If you have questions about SEO ask in the event thread and we will try to answer during the hangout on air.
Bill and Ammon's Bogus Hangout is a weekly chat and discussion event. The main crew are #SEO and #marketing folks (of some note) but guests come from all sorts of jobs and interests and our discussions may meander onto all sorts of topics.

The norm for discussion shows is to carefully pick guests and topics. That's where this show is most Bogus. This is a casual chat between interesting people and whoever joins is welcomed. Serendipitous chaos is our USP.

Our habit is to simply throw out a few invitations to guests without much (if any) ado, often without asking them in advance, and simply seeing who is free to turn up.

Regular crew:
+Bill Slawski-
+Ammon Johns-
+Kristin Drysdale-  
+Terry Van Horne- 
+Jennifer Slegg-  

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I visited the Smithsonian Air and Space Museum In Virginia just outside of DC three years ago. It was filled with great sights and is highly recommended.
19 Photos - View album

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The Calavera Nature Preserve in Carlsbad, CA.

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Entertaining d very informative. Watch it!
How To Screw Up Your Technical #SEO In Five Easy Steps -

with +Barry Adams (@badams) via +Learn Inbound

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Google's "Audio Set"

Audio Set is an ontology and human-labeled dataset for audio events that appears to be built from You Tube videos.

The Abstract from the paper tells us more:

Audio event recognition, the human-like ability to identify and re-
late sounds from audio, is a nascent problem in machine percep-
tion. Comparable problems such as object detection in images have
reaped enormous benefits from comprehensive datasets – principally
ImageNet. This paper describes the creation of Audio Set, a largescale dataset of manually-annotated audio events that endeavors to bridge the gap in data availability between image and audio research. Using a carefully structured hierarchical ontology of 632
audio classes guided by the literature and manual curation, we col-
lect data from human labelers to probe the presence of specific audio
classes in 10 second segments of YouTube videos. Segments are pro-
posed for labeling using searches based on metadata, context (e.g.,
links), and content analysis. The result is a dataset of unprecedented
breadth and size that will, we hope, substantially stimulate the de-
velopment of high-performance audio event recognizers.

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Some nice advice from John Mueller to a webmaster trying to work with a large news site; asking about getting content crawled and optimized quickly for such a site.  Nicely done, +John Mueller! :)

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