3,984 research outputs found
Demographic Inference and Representative Population Estimates from Multilingual Social Media Data
Social media provide access to behavioural data at an unprecedented scale and
granularity. However, using these data to understand phenomena in a broader
population is difficult due to their non-representativeness and the bias of
statistical inference tools towards dominant languages and groups. While
demographic attribute inference could be used to mitigate such bias, current
techniques are almost entirely monolingual and fail to work in a global
environment. We address these challenges by combining multilingual demographic
inference with post-stratification to create a more representative population
sample. To learn demographic attributes, we create a new multimodal deep neural
architecture for joint classification of age, gender, and organization-status
of social media users that operates in 32 languages. This method substantially
outperforms current state of the art while also reducing algorithmic bias. To
correct for sampling biases, we propose fully interpretable multilevel
regression methods that estimate inclusion probabilities from inferred joint
population counts and ground-truth population counts. In a large experiment
over multilingual heterogeneous European regions, we show that our demographic
inference and bias correction together allow for more accurate estimates of
populations and make a significant step towards representative social sensing
in downstream applications with multilingual social media.Comment: 12 pages, 10 figures, Proceedings of the 2019 World Wide Web
Conference (WWW '19
Listening between the Lines: Learning Personal Attributes from Conversations
Open-domain dialogue agents must be able to converse about many topics while
incorporating knowledge about the user into the conversation. In this work we
address the acquisition of such knowledge, for personalization in downstream
Web applications, by extracting personal attributes from conversations. This
problem is more challenging than the established task of information extraction
from scientific publications or Wikipedia articles, because dialogues often
give merely implicit cues about the speaker. We propose methods for inferring
personal attributes, such as profession, age or family status, from
conversations using deep learning. Specifically, we propose several Hidden
Attribute Models, which are neural networks leveraging attention mechanisms and
embeddings. Our methods are trained on a per-predicate basis to output rankings
of object values for a given subject-predicate combination (e.g., ranking the
doctor and nurse professions high when speakers talk about patients, emergency
rooms, etc). Experiments with various conversational texts including Reddit
discussions, movie scripts and a collection of crowdsourced personal dialogues
demonstrate the viability of our methods and their superior performance
compared to state-of-the-art baselines.Comment: published in WWW'1
Network Model Selection for Task-Focused Attributed Network Inference
Networks are models representing relationships between entities. Often these
relationships are explicitly given, or we must learn a representation which
generalizes and predicts observed behavior in underlying individual data (e.g.
attributes or labels). Whether given or inferred, choosing the best
representation affects subsequent tasks and questions on the network. This work
focuses on model selection to evaluate network representations from data,
focusing on fundamental predictive tasks on networks. We present a modular
methodology using general, interpretable network models, task neighborhood
functions found across domains, and several criteria for robust model
selection. We demonstrate our methodology on three online user activity
datasets and show that network model selection for the appropriate network task
vs. an alternate task increases performance by an order of magnitude in our
experiments
Transforming Graph Representations for Statistical Relational Learning
Relational data representations have become an increasingly important topic
due to the recent proliferation of network datasets (e.g., social, biological,
information networks) and a corresponding increase in the application of
statistical relational learning (SRL) algorithms to these domains. In this
article, we examine a range of representation issues for graph-based relational
data. Since the choice of relational data representation for the nodes, links,
and features can dramatically affect the capabilities of SRL algorithms, we
survey approaches and opportunities for relational representation
transformation designed to improve the performance of these algorithms. This
leads us to introduce an intuitive taxonomy for data representation
transformations in relational domains that incorporates link transformation and
node transformation as symmetric representation tasks. In particular, the
transformation tasks for both nodes and links include (i) predicting their
existence, (ii) predicting their label or type, (iii) estimating their weight
or importance, and (iv) systematically constructing their relevant features. We
motivate our taxonomy through detailed examples and use it to survey and
compare competing approaches for each of these tasks. We also discuss general
conditions for transforming links, nodes, and features. Finally, we highlight
challenges that remain to be addressed
Understanding Social Media Users via Attributes and Links
abstract: With the rise of social media, hundreds of millions of people spend countless hours all over the globe on social media to connect, interact, share, and create user-generated data. This rich environment provides tremendous opportunities for many different players to easily and effectively reach out to people, interact with them, influence them, or get their opinions. There are two pieces of information that attract most attention on social media sites, including user preferences and interactions. Businesses and organizations use this information to better understand and therefore provide customized services to social media users. This data can be used for different purposes such as, targeted advertisement, product recommendation, or even opinion mining. Social media sites use this information to better serve their users.
Despite the importance of personal information, in many cases people do not reveal this information to the public. Predicting the hidden or missing information is a common response to this challenge. In this thesis, we address the problem of predicting user attributes and future or missing links using an egocentric approach. The current research proposes novel concepts and approaches to better understand social media users in twofold including, a) their attributes, preferences, and interests, and b) their future or missing connections and interactions. More specifically, the contributions of this dissertation are (1) proposing a framework to study social media users through their attributes and link information, (2) proposing a scalable algorithm to predict user preferences; and (3) proposing a novel approach to predict attributes and links with limited information. The proposed algorithms use an egocentric approach to improve the state of the art algorithms in two directions. First by improving the prediction accuracy, and second, by increasing the scalability of the algorithms.Dissertation/ThesisDoctoral Dissertation Computer Science 201
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