6,015 research outputs found
Leveraging Social Foci for Information Seeking in Social Media
The rise of social media provides a great opportunity for people to reach out
to their social connections to satisfy their information needs. However,
generic social media platforms are not explicitly designed to assist
information seeking of users. In this paper, we propose a novel framework to
identify the social connections of a user able to satisfy his information
needs. The information need of a social media user is subjective and personal,
and we investigate the utility of his social context to identify people able to
satisfy it. We present questions users post on Twitter as instances of
information seeking activities in social media. We infer soft community
memberships of the asker and his social connections by integrating network and
content information. Drawing concepts from the social foci theory, we identify
answerers who share communities with the asker w.r.t. the question. Our
experiments demonstrate that the framework is effective in identifying
answerers to social media questions.Comment: AAAI 201
Social Search with Missing Data: Which Ranking Algorithm?
Online social networking tools are extremely popular, but can miss potential discoveries latent in the social 'fabric'. Matchmaking services which can do naive profile matching with old database technology are too brittle in the absence of key data, and even modern ontological markup, though powerful, can be onerous at data-input time. In this paper, we present a system called BuddyFinder which can automatically identify buddies who can best match a user's search requirements specified in a term-based query, even in the absence of stored user-profiles. We deploy and compare five statistical measures, namely, our own CORDER, mutual information (MI), phi-squared, improved MI and Z score, and two TF/IDF based baseline methods to find online users who best match the search requirements based on 'inferred profiles' of these users in the form of scavenged web pages. These measures identify statistically significant relationships between online users and a term-based query. Our user evaluation on two groups of users shows that BuddyFinder can find users highly relevant to search queries, and that CORDER achieved the best average ranking correlations among all seven algorithms and improved the performance of both baseline methods
Social Search: retrieving information in Online Social Platforms -- A Survey
Social Search research deals with studying methodologies exploiting social
information to better satisfy user information needs in Online Social Media
while simplifying the search effort and consequently reducing the time spent
and the computational resources utilized. Starting from previous studies, in
this work, we analyze the current state of the art of the Social Search area,
proposing a new taxonomy and highlighting current limitations and open research
directions. We divide the Social Search area into three subcategories, where
the social aspect plays a pivotal role: Social Question&Answering, Social
Content Search, and Social Collaborative Search. For each subcategory, we
present the key concepts and selected representative approaches in the
literature in greater detail. We found that, up to now, a large body of studies
model users' preferences and their relations by simply combining social
features made available by social platforms. It paves the way for significant
research to exploit more structured information about users' social profiles
and behaviors (as they can be inferred from data available on social platforms)
to optimize their information needs further
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