Location-specific tweet detection and topic summarization in Twitter

Rakesh, Vineeth; Reddy, Chandan K.; Singh, Deepesh; Ramachandran, Meera

doi:10.1145/2492517.2492583

Cited by 20 publications

(15 citation statements)

References 9 publications

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“…One is that content might change over time, and therefore new tweets might discuss new topics that the classifiers have not seen before. Another caveat is that the content of the tweet might not be location-specific; in a previous study, Rakesh et al [45] found that the content of only 289 out of 10,000 tweets was location-specific. Figure 2 shows an example of a tweet and the eight features listed above.…”

Section: )mentioning

confidence: 99%

Towards Real-Time, Country-Level Location Classification of Worldwide Tweets

Zubiaga

Voß

Procter

et al. 2017

IEEE Trans. Knowl. Data Eng.

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The increase of interest in using social media as a source for research has motivated tackling the challenge of automatically geolocating tweets, given the lack of explicit location information in the majority of tweets. In contrast to much previous work that has focused on location classification of tweets restricted to a specific country, here we undertake the task in a broader context by classifying global tweets at the country level, which is so far unexplored in a real-time scenario. We analyse the extent to which a tweet's country of origin can be determined by making use of eight tweet-inherent features for classification. Furthermore, we use two datasets, collected a year apart from each other, to analyse the extent to which a model trained from historical tweets can still be leveraged for classification of new tweets. With classification experiments on all 217 countries in our datasets, as well as on the top 25 countries, we offer some insights into the best use of tweet-inherent features for an accurate country-level classification of tweets. We find that the use of a single feature, such as the use of tweet content alone -the most widely used feature in previous work -leaves much to be desired. Choosing an appropriate combination of both tweet content and metadata can actually lead to substantial improvements of between 20% and 50%. We observe that tweet content, the user's self-reported location and the user's real name, all of which are inherent in a tweet and available in a real-time scenario, are particularly useful to determine the country of origin. We also experiment on the applicability of a model trained on historical tweets to classify new tweets, finding that the choice of a particular combination of features whose utility does not fade over time can actually lead to comparable performance, avoiding the need to retrain. However, the difficulty of achieving accurate classification increases slightly for countries with multiple commonalities, especially for English and Spanish speaking countries.

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Section: )mentioning

confidence: 99%

Towards Real-Time, Country-Level Location Classification of Worldwide Tweets

Zubiaga

Voß

Procter

et al. 2017

IEEE Trans. Knowl. Data Eng.

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“…The technique proposed by Rakesh, Reddy, Singh, and Ramachandran () generates a summary of tweets that are specific to a location. Specifically, it leverages on the tweets content and the network information of users to identify location‐specific tweets.…”

Section: Related Workmentioning

confidence: 99%

On‐demand recent personal tweets summarization on mobile devices

Chin

Bhowmick

Jatowt

2019

Asso for Info Science & Tech

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Tweets summarization aims to find a group of representative tweets for a specific set of input tweets or a given topic. In recent times, there have been several research efforts toward devising a variety of techniques to summarize tweets in Twitter. However, these techniques are either not personal (that is, consider only tweets in the timeline of a specific user) or are too expensive to be realized on a mobile device. Given that 80% of active Twitter users access the site on mobile devices, in this article we present a lightweight, personal, on‐demand, topic modeling‐based tweets summarization engine called TOTEM, designed for such devices. Specifically, TOTEM first preprocesses recent tweets in a user's timeline and exploits Latent Dirichlet Allocation‐based topic modeling to assign each preprocessed tweet to a topic. Then it generates a ranked list of relevant tweets, a topic label, and a topic summary for each of the topics. Our experimental study with real‐world data sets demonstrates the superiority of TOTEM.

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“…Bigram sequences are the different combination of keywords, numbers, hashtags used by users while tweeting. [8] Twitter data is known for its diverse uses, it is also used to predict real time events. One such model is Temporal model, which describes the time series of data.…”

Section: Related Workmentioning

confidence: 99%

TweetRush: A Tool for Analysis of Twitter Data

Dawar¹,

Student²,

Purwar³

et al. 2018

IJEME

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Twitter network has millions of users spreading information in the form of 140 character messages called tweets. And each user expresses his or her opinion with the tweet, these tweets have been used to know a person's state of mind, get recommendations and also predict the pattern. But a research is an effective one only if its results can be easily understood and a clear understanding requires visualization of the inferences. There isn't any data-graph, pie chart or a tree depicting the results of twitter analysis. Hence this paper suggests "TweetRush" tool to analyze twitter data. It is able to find the influence of a particular user in his network. Graphs help us determine a user's outreach. This tool will help advertisers to target the exact audience; budding entrepreneurs can make use of the influential factor to market their start-ups.

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Location-specific tweet detection and topic summarization in Twitter

Cited by 20 publications

References 9 publications

Towards Real-Time, Country-Level Location Classification of Worldwide Tweets

Towards Real-Time, Country-Level Location Classification of Worldwide Tweets

On‐demand recent personal tweets summarization on mobile devices

TweetRush: A Tool for Analysis of Twitter Data

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