151 research outputs found
Truth Discovery in Big Data Social Media Application
In this system first one is “misinformation spread” where a significant number of sources are contributing to false claims, making the identification of truthful claims difficult. For example, on, Instagram, rumors, Twitter scams, and influence bots are common examples of sources colluding, either intentionally or unintentionally, to spread misinformation and obscure the truth. The challenge is “data sparsity” or the “long-tail phenomenon” where a majority of sources only contribute a small number of claims, providing insufficient evidence to determine those sources’ trustworthiness. For example, in the Twitter datasets that we collected during real-world events, more than 90only contributed to a single claim. Third, many current solutions are not scalable to large-scale social sensing events because of the centralized nature of their truth discovery algorithms. We are going develop a Scalable and Robust Truth Discovery (SRTD) scheme to address the above all challenges. In this, the SRTD scheme jointly quantifies both the reliability of sources and the credibility of claims using a principled approach
OntoDSumm : Ontology based Tweet Summarization for Disaster Events
The huge popularity of social media platforms like Twitter attracts a large
fraction of users to share real-time information and short situational messages
during disasters. A summary of these tweets is required by the government
organizations, agencies, and volunteers for efficient and quick disaster
response. However, the huge influx of tweets makes it difficult to manually get
a precise overview of ongoing events. To handle this challenge, several tweet
summarization approaches have been proposed. In most of the existing
literature, tweet summarization is broken into a two-step process where in the
first step, it categorizes tweets, and in the second step, it chooses
representative tweets from each category. There are both supervised as well as
unsupervised approaches found in literature to solve the problem of first step.
Supervised approaches requires huge amount of labelled data which incurs cost
as well as time. On the other hand, unsupervised approaches could not clusters
tweet properly due to the overlapping keywords, vocabulary size, lack of
understanding of semantic meaning etc. While, for the second step of
summarization, existing approaches applied different ranking methods where
those ranking methods are very generic which fail to compute proper importance
of a tweet respect to a disaster. Both the problems can be handled far better
with proper domain knowledge. In this paper, we exploited already existing
domain knowledge by the means of ontology in both the steps and proposed a
novel disaster summarization method OntoDSumm. We evaluate this proposed method
with 4 state-of-the-art methods using 10 disaster datasets. Evaluation results
reveal that OntoDSumm outperforms existing methods by approximately 2-66% in
terms of ROUGE-1 F1 score
Sentiment Analysis on Financial News and Microblogs
Sentiment analysis is useful for multiple tasks including customer satisfaction metrics, identifying market trends for any industry or products, analyzing reviews from social media comments. This thesis highlights the importance of sentiment analysis, provides a summary of seminal works and different approaches towards sentiment analysis. It aims to address sentiment analysis on financial news and microblogs by classifying textual data from financial news and microblogs as positive or negative. Sentiment analysis is performed by making use of paragraph vectors and logistic regression in this thesis and it aims to compare it with previously performed approaches to performing analysis and help researchers in this field. This approach achieves state of the art results for the dataset used in this research. It also presents an insightful analysis of the results of this approach
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