• Corpus ID: 14601210

An Overview of Topic Discovery in Twitter Communication through Social Media Analytics

@inproceedings{Chinnov2015AnOO,
  title={An Overview of Topic Discovery in Twitter Communication through Social Media Analytics},
  author={Andrey Chinnov and Pascal Kerschke and Christian Meske and Stefan Stieglitz and Heike Trautmann},
  booktitle={AMCIS},
  year={2015}
}
The need for automatic methods of topic discovery in the Internet grows exponentially with the amount of available textual information. Nowadays it becomes impossible to manually read even a small part of the information in order to reveal the underlying topics. Social media provide us with a great pool of user generated content, where topic discovery may be extremely useful for businesses, politicians, researchers, and other stakeholders. However, conventional topic discovery methods, which… 

Characterizing Long-Running Political Phenomena on Social Media

TLDR
A set of approaches to analyze long-running political events on social media with a real-world experiment: the debate about Brexit, i.e., the process through which the United Kingdom activated the option of leaving the European Union is proposed.

Comparison of Topic modelling Techniques in Marketing - Results from an Analysis of distinctive Use Cases

TLDR
A comparison of three different topic modelling techniques (LDA, PAM, DMR) to give recommendations for three use cases identified in the literature: content extraction, trend analysis and content structuring.

Geotemporal analysis and topic modelling of Twitter data: study in nine big city areas of Indonesia

TLDR
The study results show that, in general, big cities in Indonesia have almost the same temporal curve and the peak time for making geotagged tweets occurs from 4 pm to 8 pm, which points out that a high number of the population in a city does not always produce aHigh number of Tweets.

HTwitt: a hadoop-based platform for analysis and visualization of streaming Twitter data

TLDR
The main contribution of the paper is to propose a framework for building landslide early warning systems by pinpointing useful tweets and visualizing them along with the processed information, which meets the requirement of a Hadoop-based classification system.

Social Media Analytics - Factors affecting business and IT alignment of Social Media Analytics in organizations

Social Media Analytic (SMA) is an emerging trend driven by practitioners to access and ana-lyze the increased volumes of user-generated content. The utilization of SMA provides organ-izations with

Efficient Twitter Data Cleansing Model for Data Analysis of the Pandemic Tweets

TLDR
The experiment results seem to indicate that the accuracy of sentiment classification increases once the data quality problems associated with the Twitter text are solved, and this model can correct a wide variety of anomalies from slangs, typos, Elongated (repeated Characters), transposition, Concatenated words, complex spelling mistakes as unorthodox use of acronyms.

Hydria: An Online Data Lake for Multi-Faceted Analytics in the Cultural Heritage Domain

TLDR
Hydria is the first solution in the literature that focuses on collecting, managing, analyzing, and sharing diverse, multi-faceted data in the cultural heritage domain and targets users without an IT background.

References

SHOWING 1-10 OF 68 REFERENCES

Sensing Trending Topics in Twitter

TLDR
It is found that standard natural language processing techniques can perform well for social streams on very focused topics, but novel techniques designed to mine the temporal distribution of concepts are needed to handle more heterogeneous streams containing multiple stories evolving in parallel.

Empirical study of topic modeling in Twitter

TLDR
It is shown that by training a topic model on aggregated messages the authors can obtain a higher quality of learned model which results in significantly better performance in two real-world classification problems.

Comparing Twitter and Traditional Media Using Topic Models

TLDR
This paper empirically compare the content of Twitter with a traditional news medium, New York Times, using unsupervised topic modeling, and finds interesting and useful findings for downstream IR or DM applications.

Discovering geographical topics in the twitter stream

TLDR
An algorithm is presented by modeling diversity in tweets based on topical diversity, geographical diversity, and an interest distribution of the user by exploiting sparse factorial coding of the attributes, thus allowing it to deal with a large and diverse set of covariates efficiently.

Characterizing Microblogs with Topic Models

TLDR
A scalable implementation of a partially supervised learning model (Labeled LDA) that maps the content of the Twitter feed into dimensions that correspond roughly to substance, style, status, and social characteristics of posts is presented.

Knowledge discovery in hashtags#

TLDR
This paper attempts to apply knowledge discovery process on Twitter dataset comprising hashtags along with the visual analytic techniques to provide information to the people in such a way so that they understand concealed knowledge in the data effortlessly and meritoriously.

Finding Core Topics: Topic Extraction with Clustering on Tweet

TLDR
This work proposes a simple and novel method called Core-Topic-based Clustering (CTC), which extracts topics and cluster tweets simultaneously based on the clustering principles: minimizing the inter-cluster similarity and maximizing the intra-clusters similarity.

Improving LDA topic models for microblogs via tweet pooling and automatic labeling

TLDR
This paper empirically establishes that a novel method of tweet pooling by hashtags leads to a vast improvement in a variety of measures for topic coherence across three diverse Twitter datasets in comparison to an unmodified LDA baseline and a range of pooling schemes.

TM-LDA: efficient online modeling of latent topic transitions in social media

TLDR
Temporal-LDA significantly outperforms state-of-the-art static LDA models for estimating the topic distribution of new documents over time and is able to highlight interesting variations of common topic transitions, such as the differences in the work-life rhythm of cities, and factors associated with area-specific problems and complaints.

TwitterRank: finding topic-sensitive influential twitterers

TLDR
Experimental results show that TwitterRank outperforms the one Twitter currently uses and other related algorithms, including the original PageRank and Topic-sensitive PageRank, which is proposed to measure the influence of users in Twitter.
...