60,999 research outputs found
Effectively predicting whether and when a topic will become prevalent in a social network
Copyright © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Effective forecasting of future prevalent topics plays an important role in social network business development. It involves two challenging aspects: predicting whether a topic will become prevalent, and when. This cannot be directly handled by the existing algorithms in topic modeling, item recommendation and action forecasting. The classic forecasting framework based on time series models may be able to predict a hot topic when a series of periodical changes to user-addressed frequency in a systematic way. However, the frequency of topics discussed by users often changes irregularly in social networks. In this paper, a generic probabilistic framework is proposed for hot topic prediction, and machine learning methods are explored to predict hot topic patterns. Two effective models, PreWHether and PreWHen, are introduced to predict whether and when a topic will become prevalent. In the PreWHether model, we simulate the constructed features of previously observed frequency changes for better prediction. In the PreWHen model, distributions of time intervals associated with the emergence to prevalence of a topic are modeled. Extensive experiments on real dataseis demonstrate that our method outperforms the baselines and generates more effective predictions
DeepInf: Social Influence Prediction with Deep Learning
Social and information networking activities such as on Facebook, Twitter,
WeChat, and Weibo have become an indispensable part of our everyday life, where
we can easily access friends' behaviors and are in turn influenced by them.
Consequently, an effective social influence prediction for each user is
critical for a variety of applications such as online recommendation and
advertising.
Conventional social influence prediction approaches typically design various
hand-crafted rules to extract user- and network-specific features. However,
their effectiveness heavily relies on the knowledge of domain experts. As a
result, it is usually difficult to generalize them into different domains.
Inspired by the recent success of deep neural networks in a wide range of
computing applications, we design an end-to-end framework, DeepInf, to learn
users' latent feature representation for predicting social influence. In
general, DeepInf takes a user's local network as the input to a graph neural
network for learning her latent social representation. We design strategies to
incorporate both network structures and user-specific features into
convolutional neural and attention networks. Extensive experiments on Open
Academic Graph, Twitter, Weibo, and Digg, representing different types of
social and information networks, demonstrate that the proposed end-to-end
model, DeepInf, significantly outperforms traditional feature engineering-based
approaches, suggesting the effectiveness of representation learning for social
applications.Comment: 10 pages, 5 figures, to appear in KDD 2018 proceeding
Application of Natural Language Processing to Determine User Satisfaction in Public Services
Research on customer satisfaction has increased substantially in recent
years. However, the relative importance and relationships between different
determinants of satisfaction remains uncertain. Moreover, quantitative studies
to date tend to test for significance of pre-determined factors thought to have
an influence with no scalable means to identify other causes of user
satisfaction. The gaps in knowledge make it difficult to use available
knowledge on user preference for public service improvement. Meanwhile, digital
technology development has enabled new methods to collect user feedback, for
example through online forums where users can comment freely on their
experience. New tools are needed to analyze large volumes of such feedback. Use
of topic models is proposed as a feasible solution to aggregate open-ended user
opinions that can be easily deployed in the public sector. Generated insights
can contribute to a more inclusive decision-making process in public service
provision. This novel methodological approach is applied to a case of service
reviews of publicly-funded primary care practices in England. Findings from the
analysis of 145,000 reviews covering almost 7,700 primary care centers indicate
that the quality of interactions with staff and bureaucratic exigencies are the
key issues driving user satisfaction across England
Are anonymity-seekers just like everybody else? An analysis of contributions to Wikipedia from Tor
User-generated content sites routinely block contributions from users of
privacy-enhancing proxies like Tor because of a perception that proxies are a
source of vandalism, spam, and abuse. Although these blocks might be effective,
collateral damage in the form of unrealized valuable contributions from
anonymity seekers is invisible. One of the largest and most important
user-generated content sites, Wikipedia, has attempted to block contributions
from Tor users since as early as 2005. We demonstrate that these blocks have
been imperfect and that thousands of attempts to edit on Wikipedia through Tor
have been successful. We draw upon several data sources and analytical
techniques to measure and describe the history of Tor editing on Wikipedia over
time and to compare contributions from Tor users to those from other groups of
Wikipedia users. Our analysis suggests that although Tor users who slip through
Wikipedia's ban contribute content that is more likely to be reverted and to
revert others, their contributions are otherwise similar in quality to those
from other unregistered participants and to the initial contributions of
registered users.Comment: To appear in the IEEE Symposium on Security & Privacy, May 202
Comparative social capital: Networks of entrepreneurs and investors in China and Russia
Most studies on entrepreneurs’ networks incorporate social capital and networks as independent variables that affect entrepreneurs’ actions and its outcomes. By contrast, this article examines social capital of the Chinese and Russian entrepreneurs and venture capitalists as dependent variables, and it examines entrepreneurs’ social capital from the perspectives of institutional theory and cultural theory. The empirical data are composed of structured telephone interviews with 159 software entrepreneurs, and the data of 124 venture capital decisions in Beijing and Moscow. The study found that social networks of the Chinese entrepreneurs are smaller in size, denser in structure, and more homogeneous in composition compared to networks of the Russian entrepreneurs due to the institutional and cultural differences between the two countries. Furthermore, the study revealed that dyadic (two-person) ties are stronger and interpersonal trust is greater in China than in Russia. The research and practical implications are discussed.http://deepblue.lib.umich.edu/bitstream/2027.42/40169/3/wp783.pd
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