72,853 research outputs found
Quantifying discrepancies in opinion spectra from online and offline networks
Online social media such as Twitter are widely used for mining public
opinions and sentiments on various issues and topics. The sheer volume of the
data generated and the eager adoption by the online-savvy public are helping to
raise the profile of online media as a convenient source of news and public
opinions on social and political issues as well. Due to the uncontrollable
biases in the population who heavily use the media, however, it is often
difficult to measure how accurately the online sphere reflects the offline
world at large, undermining the usefulness of online media. One way of
identifying and overcoming the online-offline discrepancies is to apply a
common analytical and modeling framework to comparable data sets from online
and offline sources and cross-analyzing the patterns found therein. In this
paper we study the political spectra constructed from Twitter and from
legislators' voting records as an example to demonstrate the potential limits
of online media as the source for accurate public opinion mining.Comment: 10 pages, 4 figure
Measuring relative opinion from location-based social media: A case study of the 2016 U.S. presidential election
Social media has become an emerging alternative to opinion polls for public
opinion collection, while it is still posing many challenges as a passive data
source, such as structurelessness, quantifiability, and representativeness.
Social media data with geotags provide new opportunities to unveil the
geographic locations of users expressing their opinions. This paper aims to
answer two questions: 1) whether quantifiable measurement of public opinion can
be obtained from social media and 2) whether it can produce better or
complementary measures compared to opinion polls. This research proposes a
novel approach to measure the relative opinion of Twitter users towards public
issues in order to accommodate more complex opinion structures and take
advantage of the geography pertaining to the public issues. To ensure that this
new measure is technically feasible, a modeling framework is developed
including building a training dataset by adopting a state-of-the-art approach
and devising a new deep learning method called Opinion-Oriented Word Embedding.
With a case study of the tweets selected for the 2016 U.S. presidential
election, we demonstrate the predictive superiority of our relative opinion
approach and we show how it can aid visual analytics and support opinion
predictions. Although the relative opinion measure is proved to be more robust
compared to polling, our study also suggests that the former can advantageously
complement the later in opinion prediction
Exploring College Students\u27 Perceptions of College Gun Violence: A Meta-Analysis
College gun violence has been one of our nation’s most forefront issues in recent years. The present meta-analysis reviewed studies reporting college students’ perceptions about school/college gun violence in order to summarize the total effect and direction of these research findings. There were only two studies identified for this research topic: Study A and Study B. Combining the effect sizes from both studies could not be accomplished because there was not enough data from Study A to calculate the correlation coefficient(r) or standardized mean difference (SMD). The summary of the results of the two studies as a metaanalysis was more a descriptive analysis. With the exception of the more conservative study participants, participants in both studies felt the most significant variable in the occurrence of school/college gun violence was the mental health of the individual perpetrator
How did the discussion go: Discourse act classification in social media conversations
We propose a novel attention based hierarchical LSTM model to classify
discourse act sequences in social media conversations, aimed at mining data
from online discussion using textual meanings beyond sentence level. The very
uniqueness of the task is the complete categorization of possible pragmatic
roles in informal textual discussions, contrary to extraction of
question-answers, stance detection or sarcasm identification which are very
much role specific tasks. Early attempt was made on a Reddit discussion
dataset. We train our model on the same data, and present test results on two
different datasets, one from Reddit and one from Facebook. Our proposed model
outperformed the previous one in terms of domain independence; without using
platform-dependent structural features, our hierarchical LSTM with word
relevance attention mechanism achieved F1-scores of 71\% and 66\% respectively
to predict discourse roles of comments in Reddit and Facebook discussions.
Efficiency of recurrent and convolutional architectures in order to learn
discursive representation on the same task has been presented and analyzed,
with different word and comment embedding schemes. Our attention mechanism
enables us to inquire into relevance ordering of text segments according to
their roles in discourse. We present a human annotator experiment to unveil
important observations about modeling and data annotation. Equipped with our
text-based discourse identification model, we inquire into how heterogeneous
non-textual features like location, time, leaning of information etc. play
their roles in charaterizing online discussions on Facebook
- …