63,273 research outputs found
Word Adjacency Graph Modeling: Separating Signal From Noise in Big Data
There is a need to develop methods to analyze Big Data to inform patient-centered interventions for better health outcomes. The purpose of this study was to develop and test a method to explore Big Data to describe salient health concerns of people with epilepsy. Specifically, we used Word Adjacency Graph modeling to explore a data set containing 1.9 billion anonymous text queries submitted to the ChaCha question and answer service to (a) detect clusters of epilepsy-related topics, and (b) visualize the range of epilepsy-related topics and their mutual proximity to uncover the breadth and depth of particular topics and groups of users. Applied to a large, complex data set, this method successfully identified clusters of epilepsy-related topics while allowing for separation of potentially non-relevant topics. The method can be used to identify patient-driven research questions from large social media data sets and results can inform the development of patient-centered interventions
A Wikipedia Literature Review
This paper was originally designed as a literature review for a doctoral
dissertation focusing on Wikipedia. This exposition gives the structure of
Wikipedia and the latest trends in Wikipedia research
Analyzing the Targets of Hate in Online Social Media
Social media systems allow Internet users a congenial platform to freely
express their thoughts and opinions. Although this property represents
incredible and unique communication opportunities, it also brings along
important challenges. Online hate speech is an archetypal example of such
challenges. Despite its magnitude and scale, there is a significant gap in
understanding the nature of hate speech on social media. In this paper, we
provide the first of a kind systematic large scale measurement study of the
main targets of hate speech in online social media. To do that, we gather
traces from two social media systems: Whisper and Twitter. We then develop and
validate a methodology to identify hate speech on both these systems. Our
results identify online hate speech forms and offer a broader understanding of
the phenomenon, providing directions for prevention and detection approaches.Comment: Short paper, 4 pages, 4 table
Negative emotions boost users activity at BBC Forum
We present an empirical study of user activity in online BBC discussion
forums, measured by the number of posts written by individual debaters and the
average sentiment of these posts. Nearly 2.5 million posts from over 18
thousand users were investigated. Scale free distributions were observed for
activity in individual discussion threads as well as for overall activity. The
number of unique users in a thread normalized by the thread length decays with
thread length, suggesting that thread life is sustained by mutual discussions
rather than by independent comments. Automatic sentiment analysis shows that
most posts contain negative emotions and the most active users in individual
threads express predominantly negative sentiments. It follows that the average
emotion of longer threads is more negative and that threads can be sustained by
negative comments. An agent based computer simulation model has been used to
reproduce several essential characteristics of the analyzed system. The model
stresses the role of discussions between users, especially emotionally laden
quarrels between supporters of opposite opinions, and represents many observed
statistics of the forum.Comment: 29 pages, 6 figure
Discussion quality diffuses in the digital public square
Studies of online social influence have demonstrated that friends have
important effects on many types of behavior in a wide variety of settings.
However, we know much less about how influence works among relative strangers
in digital public squares, despite important conversations happening in such
spaces. We present the results of a study on large public Facebook pages where
we randomly used two different methods--most recent and social feedback--to
order comments on posts. We find that the social feedback condition results in
higher quality viewed comments and response comments. After measuring the
average quality of comments written by users before the study, we find that
social feedback has a positive effect on response quality for both low and high
quality commenters. We draw on a theoretical framework of social norms to
explain this empirical result. In order to examine the influence mechanism
further, we measure the similarity between comments viewed and written during
the study, finding that similarity increases for the highest quality
contributors under the social feedback condition. This suggests that, in
addition to norms, some individuals may respond with increased relevance to
high-quality comments.Comment: 10 pages, 6 figures, 2 table
Detecting and Explaining Crisis
Individuals on social media may reveal themselves to be in various states of
crisis (e.g. suicide, self-harm, abuse, or eating disorders). Detecting crisis
from social media text automatically and accurately can have profound
consequences. However, detecting a general state of crisis without explaining
why has limited applications. An explanation in this context is a coherent,
concise subset of the text that rationalizes the crisis detection. We explore
several methods to detect and explain crisis using a combination of neural and
non-neural techniques. We evaluate these techniques on a unique data set
obtained from Koko, an anonymous emotional support network available through
various messaging applications. We annotate a small subset of the samples
labeled with crisis with corresponding explanations. Our best technique
significantly outperforms the baseline for detection and explanation.Comment: Accepted at CLPsych, ACL workshop. 8 pages, 5 figure
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