28,031 research outputs found
A Survey of Location Prediction on Twitter
Locations, e.g., countries, states, cities, and point-of-interests, are
central to news, emergency events, and people's daily lives. Automatic
identification of locations associated with or mentioned in documents has been
explored for decades. As one of the most popular online social network
platforms, Twitter has attracted a large number of users who send millions of
tweets on daily basis. Due to the world-wide coverage of its users and
real-time freshness of tweets, location prediction on Twitter has gained
significant attention in recent years. Research efforts are spent on dealing
with new challenges and opportunities brought by the noisy, short, and
context-rich nature of tweets. In this survey, we aim at offering an overall
picture of location prediction on Twitter. Specifically, we concentrate on the
prediction of user home locations, tweet locations, and mentioned locations. We
first define the three tasks and review the evaluation metrics. By summarizing
Twitter network, tweet content, and tweet context as potential inputs, we then
structurally highlight how the problems depend on these inputs. Each dependency
is illustrated by a comprehensive review of the corresponding strategies
adopted in state-of-the-art approaches. In addition, we also briefly review two
related problems, i.e., semantic location prediction and point-of-interest
recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur
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
Twitter Watch: Leveraging Social Media to Monitor and Predict Collective-Efficacy of Neighborhoods
Sociologists associate the spatial variation of crime within an urban
setting, with the concept of collective efficacy. The collective efficacy of a
neighborhood is defined as social cohesion among neighbors combined with their
willingness to intervene on behalf of the common good. Sociologists measure
collective efficacy by conducting survey studies designed to measure
individuals' perception of their community. In this work, we employ the curated
data from a survey study (ground truth) and examine the effectiveness of
substituting costly survey questionnaires with proxies derived from social
media. We enrich a corpus of tweets mentioning a local venue with several
linguistic and topological features. We then propose a pairwise learning to
rank model with the goal of identifying a ranking of neighborhoods that is
similar to the ranking obtained from the ground truth collective efficacy
values. In our experiments, we find that our generated ranking of neighborhoods
achieves 0.77 Kendall tau-x ranking agreement with the ground truth ranking.
Overall, our results are up to 37% better than traditional baselines.Comment: 10 pages, 7 figure
Data-driven Computational Social Science: A Survey
Social science concerns issues on individuals, relationships, and the whole
society. The complexity of research topics in social science makes it the
amalgamation of multiple disciplines, such as economics, political science, and
sociology, etc. For centuries, scientists have conducted many studies to
understand the mechanisms of the society. However, due to the limitations of
traditional research methods, there exist many critical social issues to be
explored. To solve those issues, computational social science emerges due to
the rapid advancements of computation technologies and the profound studies on
social science. With the aids of the advanced research techniques, various
kinds of data from diverse areas can be acquired nowadays, and they can help us
look into social problems with a new eye. As a result, utilizing various data
to reveal issues derived from computational social science area has attracted
more and more attentions. In this paper, to the best of our knowledge, we
present a survey on data-driven computational social science for the first time
which primarily focuses on reviewing application domains involving human
dynamics. The state-of-the-art research on human dynamics is reviewed from
three aspects: individuals, relationships, and collectives. Specifically, the
research methodologies used to address research challenges in aforementioned
application domains are summarized. In addition, some important open challenges
with respect to both emerging research topics and research methods are
discussed.Comment: 28 pages, 8 figure
Predictive Analysis on Twitter: Techniques and Applications
Predictive analysis of social media data has attracted considerable attention
from the research community as well as the business world because of the
essential and actionable information it can provide. Over the years, extensive
experimentation and analysis for insights have been carried out using Twitter
data in various domains such as healthcare, public health, politics, social
sciences, and demographics. In this chapter, we discuss techniques, approaches
and state-of-the-art applications of predictive analysis of Twitter data.
Specifically, we present fine-grained analysis involving aspects such as
sentiment, emotion, and the use of domain knowledge in the coarse-grained
analysis of Twitter data for making decisions and taking actions, and relate a
few success stories
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Social Measurement and Causal Inference with Text
The digital age has dramatically increased access to large-scale collections of digitized text documents. These corpora include, for example, digital traces from social media, decades of archived news reports, and transcripts of spoken interactions in political, legal, and economic spheres. For social scientists, this new widespread data availability has potential for improved quantitative analysis of relationships between language use and human thought, actions, and societal structure. However, the large-scale nature of these collections means that traditional manual approaches to analyzing content are extremely costly and do not scale. Furthermore, incorporating unstructured text data into quantitative analysis is difficult due to texts’ high-dimensional nature and linguistic complexity.
This thesis blends (a) the computational strengths of natural language processing (NLP) and machine learning to automate and scale-up quantitative text analysis with (b) two themes central to social scientific studies but often under-addressed in NLP: measurement—creating quantifiable summaries of empirical phenomena—and causal inference—estimating the effects of interventions. First, we address measuring class prevalence in document collections; we contribute a generative probabilistic modeling approach to prevalence estimation and show empirically that our model is more robust to shifts in class priors between training and inference. Second, we examine cross- document entity-event measurement; we contribute an empirical pipeline and a novel latent disjunction model to identify the names of civilians killed by police from our corpus of web-scraped news reports. Third, we gather and categorize applications that use text to reduce confounding from causal estimates and contribute a list of open problems as well as guidance about data processing and evaluation decisions in this area. Finally, we contribute a new causal research design to estimate the natural indirect and direct effects of social group signals (e.g. race or gender) on conversational outcomes with separate aspects of language as causal mediators; this chapter is motivated by a theoretical case study of U.S. Supreme Court oral arguments and the effect of an advocate’s gender on interruptions from justices. We conclude by discussing the relationship between measurement and causal inference with text and future work at this intersection
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