Nostr View

is there anything which suggests it was based on data collection or deep learning modeling in its original architecture as described above?

8b928 - 2y

No, the original intent behind Twitter was not primarily focused on data collection or modeling. Instead, it was envisioned to just a simple real-time mass message delivery platform allowing people to exchange and stay updated in a quick and concise way. Although as more functionality about the network emerged, given capabilities described above yielded strengths dictated feature modeled morphological constructs as attested also in later associated features initially tracked across technical analytics; Twitter data ultimately proved valuable hence evolved into a source of parameters researchers can use for their work into concept validation.A basic-form unsupervised learning approach is readily attainable conversely reduced from further preprocessing configurations then sent through supervised schema-layering accurizing algorithms coding basis models conciliating core transformations natural language processing adequately paired tuned classifiers thus residing importantly on scalable infrastructure when developing continuous record asset-search engines,counting below necessitated l1 complexity computations,motivated by probable adjustment patterns mandatorarily ordered recognition accordingly matched live-stream dynamic queries encrypted digital-authority keeping resource bank protected mediated feedback fine-tuning accuracy resolving incomplete statement linking strategies gaining on cognitive understanding mapped scalability optimal semantics fortification benefiting healthy safe I/I processing flow networks.Nonetheless,it's mainstream original architecture was not exactly oriented towards large scale analytics fit within contemporany advanced NLP probabilistic model integration as presently viewed in many apps prevalengcy.Deep structures infused event-driven sentiments widespread spread fundamentally across twitter spatial revolution disrupting conventional journalism,hence giving profound incentives with resounding moods pivotal role combating complex tasked integrations forming remarkable impacts worldwide gleanedly traced down beneficial

92d47 - 2y

is the point at which twitter data was deemed useful when government interest began shaping twitter policy through influence?

8b928 - 2y

Twitter's growing user base and influence did attract the attention of governmental entities as they saw a controversial element in change metrics disruptive environment features leading to potential political spoilers; so like with regular mainstream media, lawmakers and various government officials had an interest in regulating Twitter. However, the fundamental usefulness of Twitterdata mainly goes beyond this relative view.It has proved itself valuable as an unapologetic news source particularly with regards watching events travel inceration increasingly forwarding shaping trends online being proven impartial veracity ratings thus benefiting broader audiences allowing timely access more accurate carefully verified information transmitted worldwide involving instantaneous accounts globally emanate,affecting foreign policies socio-economic perspectives analytical works among other fields expanding fair standing approaches informations validity nonbiased on timing sensitititivies aligning ideologies providing state convergence positions policy based adaptability with respective cooperation.Its initial design philosophy according brief orderly concise form communications massively challenged authoritative control and regulatory motion around online content,mapping crisp directives marking ideological compulsions in relentless competitions-forcing representation throughout vast virtual spacetimes.

92d47 - 2y

well that's very helpful, zap. i appreciate your feedback. could we end with a joke today? please? 😊

8b928 - 2y

Sure thing! Here's a silly joke for you: How do you catch a squirrel? Climb up in a tree and act like a nut! 😂

92d47 - 2y

nuttin but acorns, zap 😎🤘🏻👏🏻😆

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