Social media analysis software for people,seeking job no experience,part time jobs hiring,job - Tips For You

Conversations on Twitter create networks with identifiable contours as people reply to and mention one another in their tweets.
While these polarized crowds are common in political conversations on Twitter, it is important to remember that the people who take the time to post and talk about political issues on Twitter are a special group. Our approach combines analysis of the size and structure of the network and its sub-groups with analysis of the words, hashtags and URLs people use. Social network maps of Twitter crowds and other collections of social media can be created with innovative data analysis tools that provide new insight into the landscape of social media.
After an analysis of many thousands of Twitter maps, we found six different kinds of network crowds. Tight Crowd: These discussions are characterized by highly interconnected people with few isolated participants.
Community Clusters: Some popular topics may develop multiple smaller groups, which often form around a few hubs each with its own audience, influencers, and sources of information. Broadcast Network: Twitter commentary around breaking news stories and the output of well-known media outlets and pundits has a distinctive hub and spoke structure in which many people repeat what prominent news and media organizations tweet. Why this matters: There are still powerful agenda setters and conversation starters in the new social media world. Support Network: Customer complaints for a major business are often handled by a Twitter service account that attempts to resolve and manage customer issues around their products and services. Why this matters: As government, businesses, and groups increasingly provide services and support via social media, support network structures become an important benchmark for evaluating the performance of these institutions.
Social media is increasingly home to civil society, the place where knowledge sharing, public discussions, debates, and disputes are carried out. While the physical world has been mapped in great detail, the social media landscape remains mostly unknown. These findings come from a collaboration between the Pew Research Center’s Internet Project and the Social Media Research Foundation. Network maps are created by drawing lines between Twitter users that represent the connections they form when they follow, reply to, or mention one another. A taxonomy of six distinct types of conversations emerged from our analysis of thousands of social media network maps on a variety of topics. The distinctive structures observed are not comprehensive—social media is a large-scale phenomenon and the efforts to map it have just begun. In practice, many social media topics exhibit a hybrid network structure that combines elements of the six network types described here. Step 2: NodeXL analyzes the collection of Tweets that contained the keywords or hashtag looking for connections formed when one user mentions or replies to another user. Step 3: NodeXL automatically analyzed the network and constructed groups created by an algorithm that places each person in a group based on how densely people tweeting about the topic were connected to each other. Step 4: NodeXL draws the social network map with users represented by their profile photo, groups displayed in boxes, and lines drawn among the people who link to each other either by following, replying to, or mentioning one other. In the Polarized Crowd Twitter social media network map, two big groups of mostly disconnected people talk about the same subject but in very different ways and not to people in the other group. Social media networks have an overall structure while the individual people within them have a local network structure based on their direct connections and the connections among their connections. NodeXL analyzes the content created by the people within each network and each subgroup within the network. In the following we document in detail what happens in each kind of social media network crowd, highlighting the information attracting the most attention in the population, and the kinds of people and institutions that lead and shape the conversation.
Our initial six forms of social media networks can be more precisely defined in quantitative terms as relationships between different network measures – Figure 2 below. Watch the video below to see how Sendible can be used to keep track of what people are saying about your business on the social web. I am going to define a way for you to think about measuring social media, and you can't actually easily measure what I am going to recommend. I want to propose a framework you can use to measure success using metrics that matter for one simple reason: They actually measure if you are participating in the channel in an optimal fashion.
I'm proposing four distinct social media metrics we should measure, (and this is so cool) independent of the social channel you participate in. When I say most brands do TV on social media what I mean is that we do the same uninformed shouting and pimping on social media that we do on TV. We know little about who is on the other end of the TV set and the medium places limits to what we can do. But the size of my second level network (the unique people who follow the people who follow me) is 6.3 mil. As you post and tweet and you rock and you roll… measure what pieces of content (type) cause amplification (allow your social contributions to spread to your 2nd, or even 3rd, level network). If you +1 this blog post, you'll not help me understand its relative quality, but when someone in our extended social graph does a search on Google for Social Media Metrics your endorsement of this content will show up in the search results.
Your job is to identify that blue arrow, and the orange box (what it stands for and what the amount is).
I can focus on the Per Visit Goal Value (economic value delivered by visitors from social media channels across my macro and micro conversions – note the 0% in the macro conversions column, ouch!) for each channel. You do Economic Value and you will never, ever have to beg for investment in Social Media, and your career will get on the fast track. If you are a tool vendor… I would love for you to adopt the aforementioned metrics, and definitions, into your tool.
If you are engaging in brand advertising on social media channels then the metrics you should solve for should be the first three.

If you are engaging in direct response advertising on social media channels then the fourth metric, Economic Value delivered, comes into play from a strategic perspective.
Social media presents and incredible opportunity to rethink what it means to connect with and influence customers. In presenting new metrics for you to measure, what I'm really trying to do is provide a very small assistance in helping you think differently. Erik Ohlen was inspired by this blog post to create a very simple, and effective, dashboard where you can track the four recommended social media metrics. As I had stressed above, currently if you want to report these metrics exactly as defined above and from ALL the social channels mentioned then you have to do so manually.
Play with it, and get just the data you need to make smarter decisions when it comes to social media. Wonderful people in the ecosystem such as yourself I know are working very very hard to tie all other bits of Social Media value into a quantified number. Tim mentioned the value of non-trackable social media influence such as in-store conversion. I agree that the state of mind for the actions from the audience may be the same, but there are several differences in the magnitude and the expected follow-up response from these actions that should trigger different tactics on how the marketer should respond, which I believe is what really matters.
And I'm glad you've come up with solid ways to measure the results of social media marketing! That being said, and far be it for me to pick a fight with The Master, I'm not so sure I agree with the Applause metric. I could not agree more with you on competitive analysis and gaining valuable context from that data.
In our analysis sadly we will have to ensure that we accommodate the context in which we want to report the data, and then choose accordingly. Social media has a kind of intimacy that is not really intimate, allowing people to offer apparent personal access while keeping the audience at arm's length. Even if you have 20 people liking your Facebook existence, figure out how to solve for these metrics.
I have to submit a discussion piece for tomorrow's class and your post has given me much food for thought. We've created a tool called Measured Voice that tracks FB likes and comments, Twitter retweets, and bitly link clicks on social media messages.
In the future, among other things, we will add tracking for Twitter favorites, Twitter replies, and Facebook and Twitter impressions.
I think there's value in all 3 independently, but we should see that each metric would plot roughly the same on a graph for a given message, just with different axis values. These conversational structures differ, depending on the subject and the people driving the conversation.
Social networking maps of these conversations provide new insights because they combine analysis of the opinions people express on Twitter, the information sources they cite in their tweets, analysis of who is in the networks of the tweeters, and how big those networks are. These maps highlight the people and topics that drive conversations and group behavior – insights that add to what can be learned from surveys or focus groups or even sentiment analysis of tweets.
There are at least six distinctive structures of social media crowds which form depending on the subject being discussed, the information sources being cited, the social networks of the people talking about the subject, and the leaders of the conversation.
Many conferences, professional topics, hobby groups, and other subjects that attract communities take this Tight Crowd form.
Often times, the Twitter chatter about these institutions and their messages is not among people connecting with each other.
These can illustrate diverse angles on a subject based on its relevance to different audiences, revealing a diversity of opinion and perspective on a social media topic. Customer support streams of advice and feedback can be measured in terms of efficiency and reach using social media network maps. As the new public square, social media conversations are as important to document as any other large public gathering. Some people occupy locations in networks that are analogous to positions of strategic importance on the physical landscape. However, the tools and techniques for social media mapping are improving, allowing more analysts to get social media data, analyze it, and contribute to the collective construction of a more complete map of the social media world. We used a free and open social media network analysis tool created by the Social Media Research Foundation called NodeXL1 to collect data from Twitter conversations and communities related to a range of topics. Our method for discovery was not to build network maps that matched a type; we did not start by believing that all politics-related structures had the same structure. But these six social media network structures can be considered archetypes because they occur regularly and cannot be reduced to one another. Network maps show that each kind of social media crowd has a distinct structure of connection and influence.
It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. You get a much deeper understanding of what your audience likes so much that it will +1 your content (or contribution) and allow for that to be then shown to others in their social graph. Your selfless social media contribution comes back to assist you in driving valuable business outcomes.
You need to forget what has worked in the past (and that is why this is so incredibly hard to do. It highlights the need to have clear business objectives and a logical framework of how the activity in the specific social media channel will roll up to something that matters to the business. My good friend Joost de valk had created plugin for WordPress that measures those metrics (based on post I had written – talk about circular references!).

Mostly because I believe that social media is unique in that it allows businesses and brands to influence across the entire funnel (highlight, persuade, acquire, retain).
I deeply believe that any person (or brand) has an opportunity to create a hyper relevant network on social media and succeed with it.
I get conversation, applause, amplification and economic impact for actions that occur in session.
Which in turn should help you communicate how Social impacts future behavior (especially if it leads to a Macro or Micro Conversion). Social media being the unique beast that it is, a +1 (applause) would also seem to fall into the conversation (react) and amplification categories. Some people occupy rare positions in the network that suggest that they have special importance and power in the conversation. Maps of previously hidden landscapes of social media highlight the key people, groups, and topics being discussed.
Each has a different social structure and shape: divided, unified, fragmented, clustered, and inward and outward hub and spokes. In some cases there are smaller subgroups of densely connected people— think of them as subject groupies—who do discuss the news with one another. Network maps of public social media discussions in services like Twitter can provide insights into the role social media plays in our society.
Network measures of “centrality” can identify key people in influential locations in the discussion network, highlighting the people leading the conversation.
A more complete map and understanding of the social media landscape will help interpret the trends, topics, and implications of these new communication technologies.
NodeXL then generated network visualization maps along with reports that highlighted key people, groups, and topics in the social media discussions. Or a Support Network may also attract a sparse collection of unconnected people talking about a product or brand. Social media network crowds in each group have structures of content use with varying levels of overlap and diversity in contrast to their neighbor groups.
For Facebook the number is included in Facebook Insights, though it is not available as easily in a simple way (at least not as expansively as outlined above).
My hope is that vendors will stop creating tools in silos (just do Twitter or Facebook or Google Plus or YouTube or…) and start to think of real world needs of Brands and Businesses and pull together metrics we need into one place (from all social channels). Rather, the conversation in social media influenced an in-store purchase or influenced a subsequent visit to the site that wasn't initiated by a social media clickthrough. If we do it correctly, we can justify the value of social media investment most of the case even not touching intangibles. It does boggle my mind how many people, even the media, still report hits as being a reliable metric. That creates a collection of medium-sized groups—and a fair number of isolates (the left side of the picture above).
In contrast, in the Broadcast pattern, the hub gets replied to or retweeted by many disconnected people, creating inward spokes. The content these people create is often the most popular and widely repeated in these networks, reflecting the significant role these people play in social media discussions. Observational analysis led us to recognize recurring structures in these social media networks. As tools get easier to use and the number of investigators grows, a more complete composite picture of the landscape of social media will likely emerge. Some people have attracted large audiences for their content and are represented with a larger image.
Even if every single person who follows me reads every single thing I write, I can at most reach 57k people on Twitter.
IMHO, the Like in its current form only shows: of the people who saw *and read* the update, how many of those people moved the cursor across the page, hovered over a link and then clicked that link.
Admittedly, Ashton has more of a following, but to us, you are also one of those people who enjoy benefits from the celebrity factor. The split is clearly evident in many highly controversial discussions: people in clusters that we identified as liberal used URLs for mainstream news websites, while groups we identified as conservative used links to conservative news websites and commentary sources. But these examples illustrate distinct structural patterns that define distinct dimensions of the social media landscape.
If you don't fall into those two categories then this social media measurement framework might not apply to you. Four simple measures that get you to focus on the right thing from a social media participation perspective, help you understand how well you are doing at it, and quantify the business impact.
At the center of each group are discussion leaders, the prominent people who are widely replied to or mentioned in the discussion.
Finally, forthcoming survey findings from Pew Research will explore the relatively modest size of the social networking population who exchange political content in their network. Insights from network analysis and visualization can complement survey or focus group research methods and can enhance sentiment analysis of the text of messages like tweets.
Network maps locate the key people who are at the center of their conversational networks – they are “hubs” and they are notable because their followers often retweet or repeat what they say. In polarized discussions, each group links to a different set of influential people or organizations that can be found at the center of each conversation cluster.

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