Mental Health App Real Raises $37M Series B – Techcrunch

We refer to these as consumer-stage features. F), exhibiting that our options assist the varied models to achieve better separation. To determine our selection of HypHC as a dimensionality reduction approach, we in contrast its efficiency with widespread unsupervised dimensionality reduction strategies like Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), Spectral Embedding (SE) and feature Agglomeration (FA). Spectral Embedding and t-SNE are dimensionality discount strategies based on manifold learning. U: 13 dimensional consumer-degree options as described above. Feature Agglomeration applies hierarchical clustering. U). The outcomes noticed after lowering to the dimensions talked about above followed the identical sample – which was that HypHC outperformed all of the reduction strategies. For the sake of brevity, we solely current and talk about the results obtained after decreasing the dimensions to 60 (which gave the highest F1 scores general) and with the Spectral Embedding and feature Agglomeration strategies, which carried out the most effective out of our comparison strategies. HypHC, as mentioned in Section 5, takes benefit of hierarchical community detection to carry out unsupervised dimensionality reduction to higher separate classes.
In this section, we describe our function engineering course of. We use features that can simply be extracted based mostly on data captured from the user’s profile and tweet activity, and don’t delve into the precise content material of the tweets. Past analysis works have used features derived from the content of the tweets, in depth graphs of interactions between the users and extra to extract options like sentiment, emotion, lexical options and so forth. to distinguish between lessons on Twitter (Volkova and Bell, 2017, 2016). However, these can turn into sophisticated to extract given the big amounts of data concerned and complicated multi-lingual nature of the tweets. Section 6.1.1 demonstrates the importance of our options by evaluating the same with some user-stage features that past works have used to mannequin the account traits. We use this section to explain the motivation and design of our proposed interaction options to seize the nature of interactions of each user with top profiles that emerged during the overall Elections.
This class of works combines Twitter and elections to grasp totally different consumer groups on the platform, unfold of sentiment and news on the network and echo chambers and their results, to minimize the effect Twitter has on democratic processes. In different works (Chowdhury et al., 2021), authors group Twitter users into communities primarily based on their retweet and point out networks and analyze different characteristics similar to standard tweeters, domains, and hashtags. They discovered that malicious and regular accounts participate in communities which exhibit significant differences by way of in style account and hashtag usage. Work has additionally been executed on figuring out. Characterizing deleted users on social media platforms. Authors in (Bastos, 2021) discovered that a significant proportion of accounts concerned in political discourse on Twitter revolving across the Brexit referendum campaign were deleted from the platform. Volkova et al. used profile, community and habits clues, sentiment and emotion options, textual content embeddings and matters to detect accounts deleted from Twitter which were energetic in the context of the Russian-Ukranian crisis (Volkova and Bell, 2016). Twitter itself removed over 2 million accounts from the platform which were suspected to be pretend.
HypHC offers the added benefit of representing these options in a decrease dimensional house – thus serving as a dimensionality discount method. Amongst the users active on Twitter during the elections, 2.8% of the customers taking part had been suspended and 1% of the users had been deleted from the platform. We use these features to distinguish between totally different courses of users that emerged within the aftermath of the 2019 General Elections of India. We display the effectiveness of our proposed options in differentiating between regular customers (users who had been neither suspended nor deleted), suspended customers and deleted customers. Discussions on Online Social Networks (OSNs) are a key player in huge democratic processes comparable to elections. Recently, the 2020 U.S. General Elections (Chowdhury et al., 2021) and the following Capitol Riots (News, 2021) have been heavily influenced by conversations on OSNs corresponding to Twitter and Parler (Kumaraguru et al., 2021). Politicians and political handles are very lively on OSNs, particularly throughout times of democratic elections (Khan et al., 2020). The character of their engagement on these platforms is commonly an important part of their marketing campaign strategies (Katz et al., 2013). Particularly, OSNs have played an essential part within the 2014 and 2019 General Elections in India, where studies present the success of the winning get together was carefully related to their use of Twitter to engage with voters (Ahmed et al., 2016; Gupta et al., 2020b). Users of these OSNs can work together with the content shared by political parties.
Hyperbolic manifolds have additionally been used to embed information graphs (Wang et al., 2020a), images (Khrulkov et al., 2020) and phrases (Tifrea et al., 2018) in far lower dimensions. In this section we introduce and describe the dataset used and the definitions of the totally different lessons of users in our dataset, i.e. regular, suspended and deleted customers. Question-answering (Tay et al., 2018) and clustering (Monath et al., 2019) models have also leveraged hyperbolic manifolds to enhance their performance. For our work we use the ‘Analysis of General Elections 2019 in India’ (AGE2019) dataset (Gupta et al., 2020a). This dataset consists of tweets that span from February fifth 2019 to June twenty fifth 2019. This covers a time period starting from two months previous to the first polling within the elections upto one month after the outcomes of the elections were declared. The tweets are collected by querying the Twitter knowledge collection APIs to retrieve tweets having hashtags associated to the 2019 Indian General Elections.
We use Hyperbolic Hierarchical Clustering – HypHC as the hyperbolic illustration technique to capture communities and scale back dimensionality (Chami et al., 2020). HypHC supplies the inherent benefit of community detection with information encoding. The lowered dimensions within the hyperbolic manifold ensures a computationally efficient downstream job. Additionally, representations obtained using HypHC could be treated just like Euclidean embeddings. We cut back the dimensionality of our options by an element of ten without compromising on performance by leveraging hyperbolic manifolds via HypHC, all of the whereas protecting each other part in our classification pipeline unchanged from its original Euclidean implementation. A feature engineering framework that can help OSNs engineer environment friendly representations which seize the interaction patterns with prime handles. Demonstrating the applying of hyperbolic manifolds on real world social media data to cut back the computational and space complexity while effectively separating the common, suspended and deleted accounts. Our work covers two main domains: the research round classification of accounts on Twitter; and hyperbolic manifolds and their applications.

Why Microsoft Is One Of The Best Home For Linkedin

Its webpage is a cluttered mess, and even its redesigned Canada apps really feel archaic in comparison with different social networks. It’s onerous to imagine how Microsoft might make things any worse. Its $7 billion Nokia deal was a spectacular failure that forced the company to issue a $7.6 billion write-off. It additionally paid $6.2 billion for the ad company aQuantive, which resulted in its own $6.2 billion write-down. Sure, Microsoft hasn’t had the best of luck with acquisitions lately, which makes me hesitant about getting too excited for LinkedIn. But those were deals made below Ballmer, ones that in the end led to his ousting. Nadella’s Microsoft is an entirely completely different firm, one which may actually know the way to use its expensive new toy. All merchandise really useful by Engadget are selected by our editorial workforce, unbiased of our mother or father firm. Some of our tales include affiliate links. If you purchase something by way of one of these hyperlinks, we may earn an affiliate fee.
The news of Microsoft buying LinkedIn for a whopping $26.2 billion virtually looks like a gag from HBO’s Silicon Valley. Two corporations, every typically mocked for being boring in their very own method, be part of together to turn into a kind of Voltron of dull enterprise synergy. The Clippy/LinkedIn invite jokes pretty much write themselves. Yet dig a little deeper and it is easy to see why the two firms determined to group up. And there’s one huge motive this acquisition won’t go the way of Microsoft’s Nokia deal: Satya Nadella. In addition to being Microsoft’s largest acquisition ever, the LinkedIn deal is also Nadella’s largest as CEO, so the pressure is on for him to prove it’s worthwhile. Microsoft has loads of makes use of for a sturdy enterprise social community, and LinkedIn has little room to grow by itself. But even at this early stage, it’s also clear Microsoft is taking a much different strategy with LinkedIn than it did with Nokia’s phone division and even Yammer.
LinkedIn modifications that (though it is definitely no Facebook). Mostly, although, LinkedIn makes sense as a result of it suits right into Nadella’s revised mission for Microsoft: “To empower each person and each organization on the planet to achieve extra.” Again, not precisely sexy, however it’s a philosophy that cuts to the center of a newer, more productive Microsoft. He’s not out to control entire industries and crush competition like former CEO Steve Ballmer; as an alternative he’s doubling down on the corporate’s strengths. For LinkedIn, Microsoft presents peace of mind. Despite amassing 433 million users and annual income over $three billion, LinkedIn has been displaying indicators of weakness. Back in February, its stock fell forty percent in a single day after it announced a lowered forecast for the rest of the 12 months. The explanations for that are varied: LinkedIn’s premium subscriptions, its major source of income, have not been rising as quickly because it anticipated. Its advertising and marketing enterprise cannot compete with Google and Facebook relating to attracting advert dollars, as CNBC studies.
And identical to many on-line businesses, LinkedIn’s display ad revenues are also falling. Basically, LinkedIn has plateaued as an independent firm. Weiner admits as a lot in his letter to employees: “Imagine a world where we’re now not looking up at Tech Titans corresponding to Apple, Google, Microsoft, Amazon, and Facebook, and questioning what it would be prefer to operate at their extraordinary scale — because we’re considered one of them.” He additionally asks staff to consider a world where they are not just reacting to “the intensifying aggressive panorama,” where they’re “not pressured to compromise on long-time period funding” and where they don’t seem to be as simply affected by world financial changes. It has some of the very best brand recognition in the world, but it is also a social community you solely want when you’re determined for a new job. Most of the time, your interactions with LinkedIn in all probability involve getting annoyed at unwanted connection requests or the vast amounts of spam the company sends out.
For one, LinkedIn will retain its “model, culture and independence,” in line with CEO Jeff Weiner. Compare that to Nokia’s system arm, which was forcefully assimilated into Microsoft’s Canada group. And while it is actually a pricey deal, it is also one which has some quick advantages for Microsoft. It might be extremely useful for a Dynamics consumer to tug up LinkedIn information around its customers on the fly, for instance. Business intelligence is not the sexiest of subjects, however having some of the strong information troves out there may do wonders for Dynamics, Microsoft’s floundering buyer relationship administration (CRM) instrument. Nadella writes in his letter to Microsoft staff. Indeed, Microsoft appears to have more practical potential makes use of for LinkedIn than LinkedIn itself. It may very well be built-in all through Office (particularly in Outlook) and probably even Windows (as developer and writer Paul Ford imagines). Outside of Xbox Live, Microsoft has by no means had a sturdy social network and graph to make the most of.
Since then, Nintendo has launched the equally popular Brain Age 2. There are phrase and math puzzles, a piano-playing recreation, Sudoku and even a voice-recognition rock/paper/scissors game. After finishing a series of challenges, the system reveals your “Brain Age” which, participants hope, just isn’t any older than their actual age. The Wii Fit and Wii Fit Plus are both well-liked with baby boomers. Although most boomers are still a good distance from serious about nursing homes, they’ll be inspired to know that the Wii Fit recreation programs are even discovering their method into these services, serving to residents do something they never could of their youth — use a video recreation to stay limber and robust. May 2008, it also turned gaming into a manner to keep the physique match. If exercising along with an avatar on Tv continues to be not sufficient motivation to get boomers shifting, there’s a sport on the Wii that ought to get the job carried out: Just Dance.