Exemplar-Based Topic Detection in Twitter Streams (original) (raw)

One Day in Twitter: Topic Detection Via Joint Complexity

In this paper we introduce a novel method to perform topic detection in Twitter based on the recent and novel technique of Joint Complexity. Instead of relying on words as most other existing methods which use bag-ofwords or n-gram techniques, Joint Complexity relies on String Complexity which is defined as the cardinality of a set of all distinct factors, subsequences of characters, of a given string. Each short sequence of text is decomposed in linear time into a memory efficient structure called Suffix Tree and by overlapping two trees, in linear or sublinear average time, we obtain the Joint Complexity defined as the cardinality of factors that are common in both trees. The method has been extensively tested for Markov sources of any order for a finite alphabet and gave good approximation for text generation and language discrimination. One key take-away from this approach is that it is language-agnostic since we can detect similarities between two texts in any loosely character-based language. Therefore, there is no need to build any specific dictionary or stemming method. The proposed method can also be used to capture a change of topic within a conversation, as well as the style of a specific writer in a text. In this paper we exploit a dataset collected by using the Twitter streaming API for one full day, and we extract a significant number of topics for every timeslot.

Topicseg: Enhanced Tweet Distribution and Its Detection

Journal of emerging technologies and innovative research, 2017

Twitter-has concerned about lots of using people’s distribute maximum latest facts, ensuing in a huge content of facts evolved each moment. on the other hand, the short form of texts made numerous serious issues in the utilizations of Information retrieval (IR) and NLP. Now, we propose a setup for topic segmentation in a group mode, referred to as TopicSeg. By partitioning given topics into considerable fragments, the semantic and context information is properly stored and with no problem retrieved by means of the downstream utility. By increasing the total adhesiveness report score of its prospect portions is the method followed by TopicSeg to achieve the excellent tweet segmentation. The adhesiveness report considers the possibility of a section being a portion in English. After that we suggest and compare 2 fashions to get with neighborhood context with including the linguistic systems and term -dependency in a batch of twitter posts correspondingly. Tests on tweet facts models i...

Empirical Study of Topic Modeling in Twitter

Social networks such as Facebook, LinkedIn, and Twitter have been a crucial source of information for a wide spectrum of users. In Twitter, popular information that is deemed important by the community propagates through the network. Studying the characteristics of content in the messages becomes important for a number of tasks, such as breaking news detection, personalized message recommendation, friends recommendation, sentiment analysis and others. While many researchers wish to use standard text mining tools to understand messages on Twitter, the restricted length of those messages prevents them from being employed to their full potential.

TOPIC DETECTION FROM ONLINE SOCIAL MEDIA

From last few decades there is wide spread usage of social network platforms such as twitter or other micro blogging systems which contains huge amount of timely generated data. Tweeter is fastest means of information sharing where user shares event/news which take place in front of them. Thus Tweeter act as news portal where news reaches to the people within fraction of seconds. Extracting valuable information in timely manner is important because this wealthy information is useful for companies, government agencies and health organizations. Topic detection is the new research area in data mining and knowledge discovery where extracting useful and valuable information from timely generated online streams is the new challenge. In this article we survey the different algorithms used for trending topic and event detection using social media data and proposes new system for topic detection from social media. Keywords: Twitter, Topic Detection, Term Aging, Term Co-occurrences, Burstiness.

Emerging topic detection on Twitter based on temporal and social terms evaluation

Proceedings of the Tenth …, 2010

Twitter is a user-generated content system that allows its users to share short text messages, called tweets, for a variety of purposes, including daily conversations, URLs sharing and information news. Considering its world-wide distributed network of users of any age and social condition, it represents a low level news flashes portal that, in its impressive short response time, has the principal advantage.

Method for Collecting Relevant Topics from Twitter supported by Big Data

Journal of Physics: Conference Series, 2020

There is a fast increase of information and data generation in virtual environments due to microblogging sites such as Twitter, a social network that produces an average of 8, 000 tweets per second, and up to 550 million tweets per day. That’s why this and many other social networks are overloaded with content, making it difficult for users to identify information topics because of the large number of tweets related to different issues. Due to the uncertainty that harms users who created the content, this study proposes a method for inferring the most representative topics that occurred in a time period of 1 day through the selection of user profiles who are experts in sports and politics. It is calculated considering the number of times this topic was mentioned by experts in their timelines. This experiment included a dataset extracted from Twitter, which contains 10, 750 tweets related to sports and 8, 758 tweets related to politics. All tweets were obtained from user timelines se...

Topic Modelling and Event Identification from Twitter Textual Data

arXiv (Cornell University), 2016

The tremendous growth of social media content on the Internet has inspired the development of the text analytics to understand and solve real-life problems. Leveraging statistical topic modelling helps researchers and practitioners in better comprehension of textual content as well as provides useful information for further analysis. Statistical topic modelling becomes especially important when we work with large volumes of dynamic text, e.g., Facebook or Twitter datasets. In this study, we summarize the message content of four data sets of Twitter messages relating to challenging social events in Kenya. We use Latent Dirichlet Allocation (LDA) topic modelling to analyse the content. Our study uses two evaluation measures; Normalized Mutual Information (NMI) and topic coherence analysis, to select the best LDA models. The obtained LDA results show that the tool can be effectively used to extract discussion topics and summarize them for further manual analysis.

Fast, Scalable, and Context-Sensitive Detection of Trending Topics in Microblog Post Streams

ACM transactions on management information systems, 2013

Social networks, such as Twitter, can quickly and broadly disseminate news and memes across both realworld events and cultural trends. Such networks are often the best sources of up-to-the-minute information, and are therefore of considerable commercial and consumer interest. The trending topics that appear first on these networks represent an answer to the age-old query "what are people talking about?" Given the incredible volume of posts (on the order of 45,000 or more per minute), and the vast number of stories about which users are posting at any given time, it is a formidable problem to extract trending stories in real time. In this article, we describe a method and implementation for extracting trending topics from a highvelocity real-time stream of microblog posts. We describe our approach and implementation, and a set of experimental results that show that our system can accurately find "hot" stories from high-rate Twitterscale text streams.

Topic Discovery from Tweet Replies

Abstract Twitter 1 is a popular online social information network service which allows people to read and post messages up to 140 characters, known as “tweets”. In this paper, we focus on the tweets between pairs of individuals, ie, the tweet replies, and propose a generative model to discover topics among groups of twitter users. Our model has then been evaluated with a tweet dataset to show its effectiveness.

Topic Detection and Tracking Techniques on Twitter: A Systematic Review

2021

Social networks are real-time platforms formed by users involving conversations and interactions. ,is phenomenon of the new information era results in a very huge amount of data in different forms andmodalities such as text, images, videos, and voice.,e data with such characteristics are also known as big data with 5-V properties and in some cases are also referred to as social big data. To find useful information from such valuable data, many researchers tried to address different aspects of it for different modalities. In the case of text, NLP researchers conducted many research studies and scientific works to extract valuable information such as topics. Many enlightening works on different platforms of social media, like Twitter, tried to address the problem of finding important topics from different aspects and utilized it to propose solutions for diverse use cases. ,e importance of Twitter in this scope lies in its content and the behavior of its users. For example, it is also ...