#tag: Meme or event?

Kotsakos, Dimitrios; Sakkos, Panos; Katakis, Ioannis; Gunopulos, Dimitrios

doi:10.1109/asonam.2014.6921615

Cited by 6 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This account property feature extraction process is carried out according to research from [12] where the account property features, namely feature number 1 to 51% 49% 3, are obtained from the results of data extraction of 1000 tweets in each hashtag. This account property feature is also known as a community feature or attribute, which is an attribute that describes the position of an account in its community [16].…”

Section: Account Property Feature Extractionmentioning

confidence: 99%

“…Some important findings obtained from some of these researches include bot accounts having a lower ratio of the number of followers or followings compared to real accounts, posting times made by bot accounts are more regular, and bot accounts, in general, have a younger account age than the original account. In detecting buzzer accounts, the original user metadata and activity features are generally used, both on Instagram [4] and Twitter [5], [16]. But considering the phenomenon of the emergence of buzzer accounts not only in these periods, but research on the detection of the buzzer account in different periods is also interesting to do.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Buzzer Detection on Indonesian Twitter using SVM and Account Property Feature Extension

Sibaroni

Prasetiyowati

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

View full text Add to dashboard Cite

The rapid use of Twitter social media in recent times has an impact on the faster dissemination of disinformation which is very dangerous to followers. Detection of disinformation is very important to do and can be done manually by conducting in-depth information analysis. But given the huge amount of information, this approach is less effective. Another, more effective approach is to use a machine learning-based approach. Several studies on hoax information detection based on machine learning have been carried out where some studies analyze the content of a tweet and some others analyze hashtags which are the context of a tweet. The feature usually used to analyze hashtag sentiment data is the property feature of the creator's account. The creator accounts of disinformation are called buzzer accounts. This research proposes account property feature expansion of buzzer accounts combined with the SVM classifier which in several previous similar studies has a very good performance to detect the buzzer hashtag. The experimental results show that expanding the proposed feature can increase SVM's performance in detecting hashtag buzzers by more than 24% compared to using the baseline feature, and the average F1 score obtained from the combination of methods is 84%.

show abstract

Section: Account Property Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Buzzer Detection on Indonesian Twitter using SVM and Account Property Feature Extension

Sibaroni

Prasetiyowati

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

View full text Add to dashboard Cite

show abstract

“…Dimitrios kotsakos, panos sakkos, Ionnis katakis, Dimitrios [3] Highlighted on tagging the tweets. They did the hashtag analysis in which a hash (#) symbol used to indicate a special meaning of a word and tag content in social networks like twitter.…”

Section: Literature Surveymentioning

confidence: 99%

Survey: Twitter data Analysis using Opinion Mining

Adarsh¹,

Ravikumar²

2015

IJCA

View full text Add to dashboard Cite

Social Networking is the medium widely used for expressing emotions and opinions in public life through smart phones and other mediums on the Internet. Amongst the popular portals is the Twitter. Twitter has been the point of attraction to several people in research in important areas like prediction of democratic electoral events, consumer brands, movie collections at box office, stock market, celebrities etc. Opinion mining also called as sentiment analysis offers a fast and broader way of monitoring the public sentiments. In this paper, a study on various perspectives and approaches of Twitter data analysis being carried out in recent years using opinion mining is made by considering the words, retweets, hashtags and emoticons.

show abstract

“…A user can also attach a hashtag with the tweet which can provide a significant amount of information about the event (e.g., #RIP to signify that the tweet is related to someone's death). But some hashtags can also be used to promote ideas known as memes (Kotsakos et al, 2014). Event detection from tweets also faces other challenges like noisy data, informal writing, grammatical errors, and a large volume of data coming at very high velocity.…”

Section: Introductionmentioning

confidence: 99%

Untitled

Morabia¹,

Neti²,

Malapati³

et al. 2019

Proceedings of the 2019 Conference of the North

View full text Add to dashboard Cite

Event Detection has been one of the research areas in Text Mining that has attracted attention during this decade due to the widespread availability of social media data specifically twitter data. Twitter has become a major source for information about real-world events because of the use of hashtags and the small word limit of Twitter that ensures concise presentation of events. Previous works on event detection from tweets are either applicable to detect localized events or breaking news only or miss out on many important events. This paper presents the problems associated with event detection from tweets and a tweet-segmentation based system for event detection called SEDTWik, an extension to a previous work, that is able to detect newsworthy events occurring at different locations of the world from a wide range of categories. The main idea is to split each tweet and hash-tag into segments, extract bursty segments, cluster them, and summarize them. We evaluated our results on the well-known Events2012 corpus and achieved state-of-the-art results.

show abstract

#tag: Meme or event?

Cited by 6 publications

References 12 publications

Buzzer Detection on Indonesian Twitter using SVM and Account Property Feature Extension

Buzzer Detection on Indonesian Twitter using SVM and Account Property Feature Extension

Survey: Twitter data Analysis using Opinion Mining

Untitled

Contact Info

Product

Resources

About