How can we identify these pre-revenue startups with significant upside and get involved in them before this happens? I believe there are many publicly available signals that indicate how a company is doing, and I am building tools to track, measure and organize this information.
This interactive library provides researchers with an accessible overview of the different type of open social network datasets available. It was initially developed as part of a research project to outline the different types of social network datasets at the Dynamics lab in University College Dublin. As of our launch data (April 2013) some datasets have had their features extensively catalogued, while others have just the bare minimumdetails.
Predicting the popularity of content is important for both the host and users of social media sites. The challenge of this problem comes from the inequality of the popularity of con- tent. Existing methods for popularity prediction are mainly based on the quality of content, the interface of social media site to highlight contents, and the collective behavior of user- s. However, little attention is paid to the structural charac- teristics of the networks spanned by early adopters, i.e., the users who view or forward the content in the early stage of content dissemination.
In this paper, taking the Sina Weibo as a case, we empirically study whether structural character- istics can provide clues for the popularity of short messages. We find that the popularity of content is well reflected by the structural diversity of the early adopters. Experimental results demonstrate that the prediction accuracy is signif- icantly improved by incorporating the factor of structural diversity into existing methods
Sociologists distinguish between “bridging” versus “binding forms of social connections. The MLA Twitter network suggests it is used for bonding existing groups more than bridging to new connections. If the purpose of the backchannel conversation had been to strengthen existing ties, then the next step might be reaching out to connect to less-well-connected people, thereby extending the conversation to a larger community
Abstract: This paper proposes a simple social network model of occupational segregation
This paper proposes a simple social network model of occupational segregation, generated by the existence of inbreeding bias among individuals of the same social group. If network referrals are important in getting a job, then expected inbreeding bias in the social structure results in different career choices for individuals from different social groups, which further translates into stable occupational segregation equilibria within the labour market. Our framework can be regarded as complementary to existing discrimination or rational bias theories used to explain persistent observed occupational disparities between various social groups.
The idea is that we provide each entrant into a conversation or group with an accession number: the first person has accession number 1, the second person accession number 2 and so on. The accession number is plotted in rank order on the vertical y-axis, with ranked/time ordered “events” along the horizontal x-axis: utterances in a conversation for example, or posts to a forum