8 research outputs found
Characterizing and Modeling the Dynamics of Activity and Popularity
Social media, regarded as two-layer networks consisting of users and items,
turn out to be the most important channels for access to massive information in
the era of Web 2.0. The dynamics of human activity and item popularity is a
crucial issue in social media networks. In this paper, by analyzing the growth
of user activity and item popularity in four empirical social media networks,
i.e., Amazon, Flickr, Delicious and Wikipedia, it is found that cross links
between users and items are more likely to be created by active users and to be
acquired by popular items, where user activity and item popularity are measured
by the number of cross links associated with users and items. This indicates
that users generally trace popular items, overall. However, it is found that
the inactive users more severely trace popular items than the active users.
Inspired by empirical analysis, we propose an evolving model for such networks,
in which the evolution is driven only by two-step random walk. Numerical
experiments verified that the model can qualitatively reproduce the
distributions of user activity and item popularity observed in empirical
networks. These results might shed light on the understandings of micro
dynamics of activity and popularity in social media networks.Comment: 13 pages, 6 figures, 2 table
Cautious explorers generate more future academic impact
Some scientists are more likely to explore unfamiliar research topics while
others tend to exploit existing ones. In previous work, correlations have been
found between scientists' topic choices and their career performances. However,
literature has yet to untangle the intricate interplay between scientific
impact and research topic choices, where scientific exploration and
exploitation intertwine. Here we study two metrics that gauge how frequently
scientists switch topic areas and how large those jumps are, and discover that
'cautious explorers' who switch topics frequently but do so to 'close' domains
have notably better future performance and can be identified at a remarkably
early career stage. Cautious explorers who balance exploration and exploitation
in their first four career years have up to 19% more citations per future
paper. Our results suggest that the proposed metrics depict the scholarly
traits of scientists throughout their careers and provide fresh insight,
especially for nurturing junior scientists.Comment: 16 pages of main text and 94 pages of supplementary informatio
NLP Driven Models for Automatically Generating Survey Articles for Scientific Topics.
This thesis presents new methods that use natural language processing (NLP) driven models for summarizing research in scientific fields. Given a topic query in the form of a text string, we present methods for finding research articles relevant to the topic as well as summarization algorithms that use lexical and discourse information present in the text of these articles to generate coherent and readable extractive summaries of past research on the topic. In addition to summarizing prior research, good survey articles should also forecast future trends. With this motivation, we present work on forecasting future impact of scientific publications using NLP driven features.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113407/1/rahuljha_1.pd
Do scientists trace hot topics?
10.1038/srep02207Scientific Reports3