Who You Should Not Follow: Extracting Word Embeddings from Tweets to Identify Groups of Interest and Hijackers in Demonstrations

Recalde, Lorena; Mendieta, Jonathan; Boratto, Ludovico; Terán, Luis; Vaca, Carmen; Baquerizo, Gabriela

doi:10.1109/tetc.2017.2669404

Cited by 11 publications

(5 citation statements)

References 22 publications

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“…The CBOW and Skip-gram perform similarly, although Skip-gram is more useful and gives a better outcome for infrequent words [62] . In our study, we are concerned with frequent words, and therefore adopted CBOW for word2vec training.…”

Section: Methodsmentioning

confidence: 95%

Identifying DNA N4-methylcytosine sites in the rosaceae genome with a deep learning model relying on distributed feature representation

Khanal

Tayara

Zou

et al. 2021

Computational and Structural Biotechnology Journal

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Section: Methodsmentioning

confidence: 95%

Identifying DNA N4-methylcytosine sites in the rosaceae genome with a deep learning model relying on distributed feature representation

Khanal

Tayara

Zou

et al. 2021

Computational and Structural Biotechnology Journal

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“…In a different line of research, the impact of malicious accounts on political tweet sentiment was explored [36], [37]. Some of these approaches have proposed semisupervised methods (using word embeddings) to classify Twitter hashtags and detect groups of interest and potential thread hijackers commenting on political events.…”

Section: A Related Workmentioning

confidence: 99%

“…Some of these approaches have proposed semisupervised methods (using word embeddings) to classify Twitter hashtags and detect groups of interest and potential thread hijackers commenting on political events. Others have built datasets using convolutional neural networks trained on the sentiment140 dataset [36]. In another example of the use of word embeddings [38], a neural network was trained to learn word cooccurrences and generate word vectors from a corpus of four million political tweets extracted during the EU referendum of 23 June 2016.…”

Section: A Related Workmentioning

confidence: 99%

Sentiment Analysis of Political Tweets From the 2019 Spanish Elections

et al. 2021

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The use of sentiment analysis methods has increased in recent years across a wide range of disciplines. Despite the potential impact of the development of opinions during political elections, few studies have focused on the analysis of sentiment dynamics and their characterization from statistical and mathematical perspectives. In this paper, we apply a set of basic methods to analyze the statistical and temporal dynamics of sentiment analysis on political campaigns and assess their scope and limitations. To this end, we gathered thousands of Twitter messages mentioning political parties and their leaders posted several weeks before and after the 2019 Spanish presidential election. We then followed a twofold analysis strategy: (1) statistical characterization using indices derived from well-known temporal and information metrics and methods -including entropy, mutual information, and the Compounded Aggregated Positivity Index-allowing the estimation of changes in the density function of sentiment data; and (2) feature extraction from nonlinear intrinsic patterns in terms of manifold learning using autoencoders and stochastic embeddings. The results show that both the indices and the manifold features provide an informative characterization of the sentiment dynamics throughout the election period. We found measurable variations in sentiment behavior and polarity across the political parties and their leaders and observed different dynamics depending on the parties' positions on the political spectrum, their presence at the regional or national levels, and their nationalist or globalist aspirations.

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“…Then, the vector representation for t is w t . The authors of this paper have worked in tweets modeling with word2vec in previous research projects, and the detailed methodology which covers tweets cleaning/pre-processing and text modeling is explained in [15]. It is worth mentioning that the tweets are being represented as 300-dimension vectors.…”

Section: Tweets Modelingmentioning

confidence: 99%

What Kind of Content Are You Prone to Tweet? Multi-topic Preference Model for Tweeters

Recalde

Baeza–Yates

2020

Communications in Computer and Information Science

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According to tastes, a person could show preference for a given category of content to a greater or lesser extent. However, quantifying people's amount of interest in a certain topic is a challenging task, especially considering the massive digital information they are exposed to. For example, in the context of Twitter, aligned with his/her preferences a user may tweet and retweet more about technology than sports and do not share any music-related content. The problem we address in this paper is the identification of users' implicit topic preferences by analyzing the content categories they tend to post on Twitter. Our proposal is significant given that modeling their multi-topic profile may be useful to find patterns or association between preferences for categories, discover trending topics and cluster similar users to generate better group recommendations of content. In the present work, we propose a method based on the Mixed Gaussian Model to extract the multidimensional preference representation for 399 Ecuadorian tweeters concerning twentytwo different topics (or dimensions) which became known by manually categorizing 68.186 tweets. Our experiment findings indicate that the proposed approach is effective at detecting the topic interests of users.

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Who You Should Not Follow: Extracting Word Embeddings from Tweets to Identify Groups of Interest and Hijackers in Demonstrations

Cited by 11 publications

References 22 publications

Identifying DNA N4-methylcytosine sites in the rosaceae genome with a deep learning model relying on distributed feature representation

Identifying DNA N4-methylcytosine sites in the rosaceae genome with a deep learning model relying on distributed feature representation

Sentiment Analysis of Political Tweets From the 2019 Spanish Elections

What Kind of Content Are You Prone to Tweet? Multi-topic Preference Model for Tweeters

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