The document discusses two ASP.NET projects focused on mining social media data to understand student learning experiences and cost-effective viral marketing strategies in social networks. The first project integrates qualitative analysis and data mining techniques to analyze tweets related to student issues, while the second project explores the economic implications of viral marketing in the context of limited influence propagation. Both projects aim to leverage social media data for insightful analysis and optimized strategies.

1. IEEE 2014 ASP.NET with VB Projects
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List Link : http://kasanpro.com/projects-list/ieee-2014-asp-net-with-vb-projects
Title :Mining Social Media Data for Understanding Student's Learning Experiences
Language : ASP.NET with VB
Project Link :
http://kasanpro.com/p/asp-net-with-vb/mining-social-media-data-understanding-students-learning-experiences
Abstract : Students' informal conversations on social media (e.g. Twitter, Facebook) shed light into their educational
experiences - opinions, feelings, and concerns about the learning process. Data from such uninstrumented
environment can provide valuable knowledge to inform student learning. Analyzing such data, however, can be
challenging. The complexity of student's experiences reflected from social media content requires human
interpretation. However, the growing scale of data demands automatic data analysis techniques. In this paper, we
developed a workflow to integrate both qualitative analysis and large - scale data mining techniques. We focus on
engineering student's Twitter posts to understand issues and problems in their educational experiences. We first
conducted a qualitative analysis on samples taken from about 25,000 tweets related to engagement, and sleep
deprivation. Based on these results, we implemented a multi - label classification algorithm to classify tweets
reflecting student's problems. We then used the algorithm to train a detector of student problems from about 35,000
tweets streamed at the geo - location of Purdue University. This work, for the first time, presents a methodology and
results that show how informal social media data can provide insights into students' experiences.
Title :Cost-effective Viral Marketing for Time-critical Campaigns in Large-scale Social Networks
Language : ASP.NET with VB
Project Link : http://kasanpro.com/p/asp-net-with-vb/viral-marketing-cost-effective-time-critical-campaigns-large-scale-social-n
Abstract : Online social networks (OSNs) have become one of the most effective channels for marketing and
advertising. Since users are often influenced by their friends, "wordof- mouth" exchanges, so-called viral marketing, in
social networks can be used to increase product adoption or widely spread content over the network. The common
perception of viral marketing about being cheap, easy, and massively effective makes it an ideal replacement of
traditional advertising. However, recent studies have revealed that the propagation often fades quickly within only few
hops from the sources, counteracting the assumption on the self-perpetuating of influence considered in literature.
With only limited influence propagation, is massively reaching customers via viral marketing still affordable? How to
economically spend more resources to increase the spreading speed? We investigate the cost-effective massive viral
marketing problem, taking into the consideration the limited influence propagation. Both analytical analysis based on
power-law network theory and numerical analysis demonstrate that the viral marketing might involve costly seeding.
To minimize the seeding cost, we provide mathematical programming to find optimal seeding for medium-size
networks and propose VirAds, an efficient algorithm, to tackle the problem on largescale networks. VirAds guarantees
a relative error bound of O(1) from the optimal solutions in power-law networks and outperforms the greedy heuristics
which realizes on the degree centrality. Moreover, we also show that, in general, approximating the optimal seeding
within a ratio better than O(log n) is unlikely possible.