Results 1 -
6 of
6
Computing the Kullback-Leibler Divergence between two Weibull Distributions
"... ar ..."
(Show Context)
Strong Regularities in Growth and Decline of Popularity of Social Media Services
"... We analyze general trends and pattern in time series that characterize the dynamics of collective attention to social media services and Web-based businesses. Our study is based on search frequency data available from Google Trends and considers 175 different services. For each service, we collect d ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
(Show Context)
We analyze general trends and pattern in time series that characterize the dynamics of collective attention to social media services and Web-based businesses. Our study is based on search frequency data available from Google Trends and considers 175 different services. For each service, we collect data from 45 different countries as well as global av-erages. This way, we obtain more than 8,000 time series which we analyze using diffusion models from the economic sciences. We find that these models accurately characterize the empirical data and our analysis reveals that collective attention to social media grows and subsides in a highly regular and predictable manner. Regularities persist across regions, cultures, and topics and thus hint at general mech-anisms that govern the adoption of Web-based services. We discuss several cases in detail to highlight interesting find-ings. Our methods are of economic interest as they may inform investment decisions and can help assessing at what stage of the general life-cycle a Web service is at.
Diffusion of Innovations Revisited: From Social Network to Innovation Network
"... ABSTRACT The spreading of innovations among individuals and organizations in a social network has been extensively studied. Although the recent studies among the social computing and data mining communities have produced various insightful conclusions about the diffusion process of innovations by f ..."
Abstract
- Add to MetaCart
(Show Context)
ABSTRACT The spreading of innovations among individuals and organizations in a social network has been extensively studied. Although the recent studies among the social computing and data mining communities have produced various insightful conclusions about the diffusion process of innovations by focusing on the properties and evolution of social network structures, less attention has been paid to the interrelationships among the multiple innovations being diffused, such as the competitive and collaborative relationships between innovations. In this paper, we take a formal quantitative approach to address how different pieces of innovations "socialize" with each other and how the interrelationships among innovations affect users' adoption behavior, which provides a novel perspective of understanding the diffusion of innovations. Networks of innovations are constructed by mining large scale text collections in an unsupervised fashion. We are particularly interested in the following questions: what are the meaningful metrics on the network of innovations? What effects do these metrics exert on the diffusion of innovations? Do these effects vary among users with different adoption preferences or communication styles? While existing studies primarily address social influence, we provide a detailed discussion of how innovations interrelate and influence the diffusion process.
4 Computing the Kullback-Leibler Divergence between two Generalized Gamma Distributions
"... ar ..."
Crowdsourced Explanations for Humorous Internet Memes Based on Linguistic Theories
"... Humorous images can be seen in many social media web-sites. However, newcomers to these websites often have trouble fitting in because the community subculture is usu-ally implicit. Among all the types of humorous images, Internet memes are relatively hard for newcomers to un-derstand. In this work, ..."
Abstract
- Add to MetaCart
Humorous images can be seen in many social media web-sites. However, newcomers to these websites often have trouble fitting in because the community subculture is usu-ally implicit. Among all the types of humorous images, Internet memes are relatively hard for newcomers to un-derstand. In this work, we develop a system that lever-ages crowdsourcing techniques to generate explanations for memes. We claim that people who are not familiar with In-ternet meme subculture can still quickly pick up the gist of the memes by reading the explanations. Our template-based explanations illustrate the incongruity between normal situ-ations and the punchlines in jokes. The explanations can be produced by completing the two proposed human task pro-cesses. Experimental results suggest that the explanations produced by our system greatly help newcomers to under-stand unfamiliar memes. For further research, it is possi-ble to employ our explanation generation system to improve computational humanities.
