Gilad Lotan

SVP of AI and Data Science, BuzzFeed · 1 appearance on the record.

computed by AI from the episodes · how this works → · full disclaimer →

scientistexecutiveacademic@gilgul ↗LinkedIn ↗giladlotan.com ↗

Gilad Lotan leads BuzzFeed's centralized data science and AI teams, deploying machine learning, embeddings, and analytics across content, audience personalization, and advertising. He previously worked at Microsoft's FUSE Labs and SocialFlow, and is recognized for his research into social network information flows, online propaganda, and bot networks.

9statements → 5claims → 2claims resolved → 4.11/5average certainty → 1.11/5average debate potential → 1said about them ↓

1 supported 1 partly supported 0 contradicted 3 not checkable as stated how the 5 claims stand · each chip opens the sources

5 assertions · 2 insights · 2 disclosures · every statement was checked. The predictions and assertions are the 5 claims: statements the public record can support or contradict. 2 are resolved, and 3 name no date, number or outcome precise enough to check. Everything else (opinions, insights, what ifs, disclosures) can never be settled by the record, so it carries no assessment.

The record, in short

What the tape says about how Gilad argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.

Their most notable supported claim

Assertion Supported
Digg operated with only 15 employees under Betaworks in 2013
“Digg, for example, is fairly large, ah, in terms of Betaworks companies. It's 15 people.”
Gilad Lotan Dec 5, 2013 ▶ 20:58 Gilad Lotan, Betaworks // Data Driven NYC 20 // Nov 2013

Expressed certainty vs assessment result

none yet certainty 1
none yet certainty 2
none yet certainty 3
100% certainty 4
50% certainty 5

weighted support: a fully supported claim counts one, a partly supported claim counts half. Each filled bar is clickable and opens exactly those claims; "none yet" means nothing said at that certainty level has resolved yet

How they sound: speaking style how? →

239 words/min while actually speaking · 59.4 um and uh per 1k words

No argument clarity score for Gilad Lotan: no usable question→answer exchanges on raw tape (a fair score needs 8+). We do not score a sample that small. Roundtable and news formats yield far fewer direct exchanges than interviews.

Measured by listening to the audio itself: 3,352 words across 1 episode of raw-level tape, transcribed verbatim with every um and uh kept, each one attributed only where the alignment onto our timed stream is unambiguous. These are measurements of speaking style. We do not rank them: across this corpus, fluency and argument quality are nearly uncorrelated (ρ≈0.2), and smooth talking does not signal clear thinking. How it's measured →

Everything Gilad Lotan said on the MAD Podcast that made the record, most notable first. Filter by type, assessment or year in the ledger →

Disclosure
Lotan: Betaworks was an early investor in Tumblr and Kickstarter
“So we were one of the earliest investors in Tumblr and Kickstarter, and a whole bunch of, ah, companies across, ah, the tech scene in New York City.”
Gilad Lotan Dec 5, 2013 ▶ 0:56 Gilad Lotan, Betaworks // Data Driven NYC 20 // Nov 2013
Assertion Partly supported
Lotan: TweetDeck was built at Betaworks and sold to Twitter
“TweetDeck was actually built at Betaworks and then sold to Twitter.”
Gilad Lotan Dec 5, 2013 ▶ 1:13 Gilad Lotan, Betaworks // Data Driven NYC 20 // Nov 2013
Insight
Lotan: Gephi is ideal for quick exploratory graph analysis
“It's a great tool to pull in graphs and do some exploratory data analysis, so the section where you're sort of trying to explore a data set, you don't want to put too much effort into it and build something for it, you can just easily use this open source tool…”
Gilad Lotan Dec 5, 2013 ▶ 6:14 Gilad Lotan, Betaworks // Data Driven NYC 20 // Nov 2013
Insight
Lotan: Network graph analysis effectively isolates spam and off-topic data
“And it's actually also a great way to get rid of spam, things that aren't related not that I think that Python snakes are spammy, because they're pretty awesome, but it's a way to sort of, to identify them as separate from the context that we're trying to unde…”
Gilad Lotan Dec 5, 2013 ▶ 12:23 Gilad Lotan, Betaworks // Data Driven NYC 20 // Nov 2013
Assertion Not checkable as stated
Early Giphy tags were manually labeled by humans for cleaner data
“They're manually labeled, so we're getting these labels from, ah, actual humans, ah, so they're really, really clean and great data.”
Gilad Lotan Dec 5, 2013 ▶ 13:15 Gilad Lotan, Betaworks // Data Driven NYC 20 // Nov 2013
Assertion Not checkable as stated
Giphy's top tag clusters in 2013 were funny content, art, and movies
“We get three dominant sort of clusters in this data, and it's, I don't think it's surprising There's the lol, right, lots of just funny, funny stuff. There's, like, kind of pretty, beautiful content, like photography, just artistic stuff, and then lots of movi…”
Gilad Lotan Dec 5, 2013 ▶ 13:47 Gilad Lotan, Betaworks // Data Driven NYC 20 // Nov 2013
Assertion Not checkable as stated
Lotan: Giphy GIFs tagged 'funny' feature laughing people, while 'LOL' features animals
“Content that has the tag funny usually consists of sort of people laughing here, like funny in terms of funny, they're, you know, you see their face and you see them laughing. LOL tended to have, for some weird reason, have animals doing weird stuff. Or young …”
Gilad Lotan Dec 5, 2013 ▶ 15:09 Gilad Lotan, Betaworks // Data Driven NYC 20 // Nov 2013
Disclosure
Hypertable is highly efficient at writing content for graph storage
“I currently use Hypertable which is just super efficient at writing content.”
Gilad Lotan Dec 5, 2013 ▶ 19:48 Gilad Lotan, Betaworks // Data Driven NYC 20 // Nov 2013
Assertion Supported
Digg operated with only 15 employees under Betaworks in 2013
“Digg, for example, is fairly large, ah, in terms of Betaworks companies. It's 15 people.”
Gilad Lotan Dec 5, 2013 ▶ 20:58 Gilad Lotan, Betaworks // Data Driven NYC 20 // Nov 2013

The other half of the tape: Gilad Lotan's own voice is left out of every number here. Other people bring the name up 1 time in 1 episode on the MAD Podcast. every mention, with the transcript →

Who brings them up most Adam Laiacano 1

Every mention by year

tap a year for its mentions
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0112013episodes it came up in
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Appearances (1)

EpisodeDateSpeaking time
Gilad Lotan, Betaworks // Data Driven NYC 20 // Nov 2013 Dec 5, 2013 17m
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