Conversion rate measures how often your social content starts the process to a conversion event like a subscription, download, or sale. This is one of the most important social media marketing metrics because it shows the value of your social content as a means of feeding your funnel.
To calculate it, add up every mention of your brand on social across all networks. Do the same for your competitors. Add both sets of mentions together to get a total number of mentions for your industry. Divide your brand mentions by the industry total, then multiply by 100 to get your SSoV as a percentage.
In his sophomore year at Harvard, computer-science genius Mark Zuckerberg (Jesse Eisenberg) and his best friend, Eduardo Saverin (Andrew Garfield), create a site ranking their female classmates' hotness. It gets the attention of rich, entrepreneurial seniors Tyler and Cameron Winklevoss (both played by Armie Hammer) and their business partner, who hire Zuckerberg to create a social networking site for Harvard students. But instead of working on the Harvard-only site, Zuckerberg asks Saverin to front him the start-up costs to launch what they call \"thefacebook,\" which starts at Harvard but eventually spreads to other elite universities across the country. After the site hits Stanford, Zuckerberg and Saverin meet Napster co-founder Sean Parker (Justin Timberlake), who ingratiates himself into the founders' circle, usurps Saverin, and helps Zuckerberg get the funds to transform \"thefacebook\" into Facebook. In the process, Zuckerberg faces lawsuits from his Harvard rivals and his former best friend.
There was a lot of pre-release hype for THE SOCIAL NETWORK -- and for once, the buzz is well-deserved. This is truly an enthralling film; all of the pieces -- writing, plot, direction, acting, soundtrack -- create a memorable, timely movie that couldn't be more relevant to the current zeitgeist. If a story about a business' Ivy League founders or Harvard social intrigue or young billionaires in the making doesn't sound compelling, this movie will surprise you. And the credit must go to director David Fincher and writer Aaron Sorkin, who've taken what sounds like a very boring premise -- boy genius possibly steals an idea to create one of the dominating media forces of the decade -- and turned it into an award-worthy film that even Facebook objectors will enjoy.
Eisenberg plays Zuckerberg as a socially awkward computer genius who isn't an adorable geek (like many of Eisenberg's previous roles). He's a huge jerk -- or, as his date tells him in the first scene, a first-class \"a--hole\" -- obsessed with status and, later, getting back at said date for rejecting him. How many multibillion dollar ideas started out as a way to show up someone who rejected the innovator And how many business are built on the backs of broken friendships As Saverin, British import Garfield is pitch perfect. He exudes the confidence that comes with wealthy, but unlike Zuckerberg or the Winklevoss twins, he's not condescending. In many ways, he's the heart of the movie, because his character is so much more likable than Zuckerberg -- so much so that you want him to win his lawsuit against Facebook. The movie's biggest scene-stealers are Timberlake -- who's all slimy and paranoid charm as Parker -- and the Winklevoss brothers, who are played by Hammer so well that you'd swear it was twin actors. Each twin is patrician perfection personified, and the fact that their social networking idea is the seed that Zuckerberg turns into Facebook serves as a slap in the face to their entitlement. What's true and what isn't doesn't quite matter for the purposes of this film; in the end Facebook's \"status\" is bigger than all its players.
Provide exclusive contentEsurance, in partnership with Paramount, shared exclusive video content with fans on its Facebook page. This included behind-the-scenes videos and downloadable wallpaper. To add some whimsy to the social media mix, Esurance also allowed visitors to the Facebook Star Trek tab to turn themselves into a Vulcan (an alien race featured prominently in the Star Trek storyline) via the Vulcanizer app.
Jobs said Apple TV owners will be able to rent HD movies for 4.99 dollars and television shows from the Fox and ABC networks for 99 cents. US users can also stream content from movie rental service Netflix, he said.
First model: Explorative test. Hubs are interesting nodes and therefore we looked at their coverage. Tests have been done on a 100.000 size network with on average 10 links per node. These networks have been generated with an algorithm for scale-free networks and an algorithm for small-world networks. From the outcome it can be concluded that the coverage of hubs in scale free networks is far greater than those in small world networks. Also the coverage of hubs in a scale-free network outperforms the coverage of 25 leaf nodes in contrast to the small-world network.
According to research the existing social networks of Flickr, Youtube, Orkut and Livejournal are scale free and small world networks. Youtube has an interesting property where some peers are celebrities. These celebrities are highly connected but unlike hubs they do not have a higher than random chance to be connected to other highly connected peers.
The problem I foresee is that it is not clear to users why they are building a social network. This may result into users making many friends just for the fun of it or leaving the functionality for what it is and not make any friends at all. A social network can only be used to infer some kind of trust information if relationships are meaningful. Unfortunately this can not be tested or prophesied and therefore only the recommendation can be made to make friendship relations more meaningful.
Unlike friendship relations, moderator approvals are of clear use to users because it will help the user pick moderations from the same moderator for next files. Without a social network these moderator approvals are however useless, because without any ties it is hard to distinguish approvals of honest peers from collusion networks
We assume the big information providers will be in large part the hubs in the network. The reasoning behind this is that these nodes put a lot of effort into building a meaningful social network and are active in the Tribler community. This will make the chance that they produce a lot of meaningful moderator approvals larger than average.
Youtube like celebrities, be it approval or moderator providers. Celebrity moderators means many approvals which is good. Approval celebrities create a big star form network without scalefree characteristics.
Leaf nodes are less active in the network or potential hubs that have just started. These nodes are highly clustered with their friends which may or may not create specific interest clusters. This will make finding moderator approvals far more easy.The youtube dataset has favorites, but these point towards movies and not users. Could look at the correlation between amount of friends and amount of favorites to predict moderator approval behavior.
No idea.. Small message size will however hamper breadth first search of the social network. Whether this is bad remains to be seen. If hubs are the most interesting nodes peers can choose to send these relations first to direct the discovery algorithm.
If friend spam becomes common practice it should be possible to remove connections. This could be done by adding a lifetime to relationships so they have to be renewed every few days or something. This could however cause social networks to collapse if central peers remain offline longer than the lifetime of their relationships.
Without a social network peers will have trouble distinguishing good metadata approvers from bad approvers. Bartercast could help, otherwise the amount of approvals could be compared to rate moderations.
Peers that insert valuable approvals into the network may choose to befriend other peers with valuable approvals. Only if this is the case approvals be used in the SRI to determine the relevance of social paths containing 2 or more approvals. This would require a name change for SRI as it will be a specific index for approval relevance and not social relevance.
SocioCast will be tied to BuddyCast like BarterCast. Buddycast3 connects 50% of the time to random peers and 50% of the time to taste buddies. This is unwanted behavior for SocioCast, because peers should expand their social network tree. Therefore it is proposed that Buddycast4 will connect 33% of the time to random peers, 33% of the time to taste buddies and 33% of the time to peers with a high SRI.
It is hard to get a global overview of network connections free from tampering. And if unsafe relations are considered centrality and other network analyses may not be accurate enough and influenced.
We have also looked at the social network in this dataset. This is however not a complete crawl which has several implications. For each user that has been crawled it is known what his friends are. It is however possible that these friends themselves are not crawled. The networks that have been found are sparser than in reality and due to missing connections they are probably more connected than what is found in the following graphs.
We have first looked at the distribution of social network sizes. The largest network consists of 134246 users of which 105793 users are part of our dataset. That means that we have no information about outgoing and incoming connections from the remaining 28453 users.The small amount of networks of size 1 can be explained due to Youtube allowing users to add themselves as a friend. 1e1e36bf2d