Combining textual features to detect cyberbullying in social media posts

Many non-standard elements of ‘netspeak’ writing can be viewed as efforts to replicate the linguistic role played by nonverbal modalities in speech, conveying contextual information such as affect and interpersonal stance. Recently, a new non-standard communicative tool has emerged in online writing: emojis. These unicode characters contain a standardized set of pictographs, some of which are visually similar to well-known emoticons. Do emojis play the same linguistic role as emoticons and other ASCII-based writing innovations? If so, might the introduction of emojis eventually displace the earlier, user-created forms of contextual expression? Using a matching approach to causal statistical inference, we show that as social media users adopt emojis, they dramatically reduce their use of emoticons, suggesting that these linguistic resources compete for the same communicative function. Furthermore, we demonstrate that the adoption of emojis leads to a corresponding increase in the use of standard spellings, suggesting that all forms of non-standard writing are losing out in a competition with emojis. Finally, we identify specific textual features that make some emoticons especially likely to be replaced by emojis.

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CDetector: Extracting Textual Features of Financial Social Media to Detect Cyber Attacks

10.1109/icccn52240.2021.9522290 ◽

2021 ◽

Author(s):

Jinwei Liu ◽

Long Cheng ◽

Hongmei Chi ◽

Cong Liu ◽

Richard A. Alo

Keyword(s):

Social Media ◽

Cyber Attacks ◽

Textual Features

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Disaster Image Classification by Fusing Multimodal Social Media Data

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10100636 ◽

2021 ◽

Vol 10 (10) ◽

pp. 636

Author(s):

Zhiqiang Zou ◽

Hongyu Gan ◽

Qunying Huang ◽

Tianhui Cai ◽

Kai Cao

Keyword(s):

Social Media ◽

Situational Awareness ◽

Multimodal Fusion ◽

Fusion Model ◽

Multimodal Data ◽

Disaster Assessment ◽

Social Media Platforms ◽

Text Information ◽

Textual Features ◽

Media Data

Social media datasets have been widely used in disaster assessment and management. When a disaster occurs, many users post messages in a variety of formats, e.g., image and text, on social media platforms. Useful information could be mined from these multimodal data to enable situational awareness and to support decision making during disasters. However, the multimodal data collected from social media contain a lot of irrelevant and misleading content that needs to be filtered out. Existing work has mostly used unimodal methods to classify disaster messages. In other words, these methods treated the image and textual features separately. While a few methods adopted multimodality to deal with the data, their accuracy cannot be guaranteed. This research seamlessly integrates image and text information by developing a multimodal fusion approach to identify useful disaster images collected from social media platforms. In particular, a deep learning method is used to extract the visual features from social media, and a FastText framework is then used to extract the textual features. Next, a novel data fusion model is developed to combine both visual and textual features to classify relevant disaster images. Experiments on a real-world disaster dataset, CrisisMMD, are performed, and the validation results demonstrate that the method consistently and significantly outperforms the previously published state-of-the-art work by over 3%, with a performance improvement from 84.4% to 87.6%.

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Sentiment analysis of comments in social media

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v10i6.pp5917-5922 ◽

2020 ◽

Vol 10 (6) ◽

pp. 5917

Author(s):

Abdulrahman Alrumaih ◽

Ali Al-Sabbagh ◽

Ruaa Alsabah ◽

Harith Kharrufa ◽

James Baldwin

Keyword(s):

Social Media ◽

Sentiment Analysis ◽

Specific Aspect ◽

World Cup ◽

Intended Meaning ◽

Personalized Services ◽

Social Media Platforms ◽

Sentiment Orientation ◽

Textual Features ◽

Substantial Interest

Social media platforms are witnessing a significant growth in both size and purpose. One specific aspect of social media platforms is sentiment analysis, by which insights into the emotions and feelings of a person can be inferred from their posted text. Research related to sentiment analysis is acquiring substantial interest as it is a promising filed that can improve user experience and provide countless personalized services. Twitter is one of the most popular social media platforms, it has users from different regions with a variety of cultures and languages. It can thus provide valuable information for a diverse and large amount of data to be used to improve decision making. In this paper, the sentiment orientation of the textual features and emoji-based components is studied targeting “Tweets” and comments posted in Arabic on Twitter, during the 2018 world cup event. This study also measures the significance of analyzing texts including or excluding emojis. The data is obtained from thousands of extracted tweets, to find the results of sentiment analysis for texts and emojis separately. Results show that emojis support the sentiment orientation of the texts and that texts or emojis cannot separately provide reliable information as they complement each other to give the intended meaning.

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Extracting Textual Features of Financial Social Media to Detect Cognitive Hacking

2018 IEEE International Conference on Intelligence and Security Informatics (ISI) ◽

10.1109/isi.2018.8587364 ◽

2018 ◽

Cited By ~ 1

Author(s):

Wingyan Chung ◽

Jinwei Liu ◽

Xinlin Tang ◽

Vincent S. K. Lai

Keyword(s):

Social Media ◽

Textual Features

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Sarcasm Detection Engine for Twitter Sentiment Analysis using Textual and Emoji Feature

Jurnal Ilmu Komputer dan Informasi ◽

10.21609/jiki.v14i1.812 ◽

2021 ◽

Vol 14 (1) ◽

pp. 1-8

Author(s):

Bagus Satria Wiguna ◽

Cinthia Vairra Hudiyanti ◽

Alqis Alqis Rausanfita ◽

Agus Zainal Arifin

Keyword(s):

Neural Network ◽

Social Media ◽

Convolutional Neural Network ◽

Weighting Scheme ◽

Individual Group ◽

Social Media Platform ◽

Polarity Score ◽

Media Platform ◽

Textual Features ◽

Textual Feature

Twitter is a social media platform that is used to express sentiments about events, topics, individuals, and groups. Sentiments in Tweets can be classified as positive or negative expressions. However, in sentiment, there is an expression that is actually the opposite of what is mean to be, and this is called sarcasm. The existence of sarcasm in a Tweet is difficult to detect automatically by a system even by humans. In this research, we propose a weighting scheme based on inconsistency between sentimen of tweet contain in Indonesian and the usage of emoji. With the weighting scheme for the detection of sarcasm, it can be used to find out a sentiment about a event, topic, individual, group, or product's review. The proposed method is by calculating the distance between the textual feature polarity score obtained from the Convolutional Neural Network and the emoji polarity score in a Tweet. This method is used to find the boundary value between Tweets that contain sarcasm or not. The experimental results of the model developed, obtained f1-score 87.5%, precision 90.5% and recall 84.8%. By using the textual features and emoji models, it can detect sarcasm in a Tweet.

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Assessing the combination of DistilBERT news representations and difusion topological features to classify fake news

Journal of Information and Data Management ◽

10.5753/jidm.2021.1895 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Carlos Abel Córdova Sáenz ◽

Marcelo Dias ◽

Karin Becker

Keyword(s):

Social Media ◽

Deep Learning ◽

State Of The Art ◽

Fake News ◽

Topological Features ◽

Compact Representations ◽

Fact Checking ◽

Textual Features ◽

Propagation Network

Fake news (FN) have affected people’s lives in unimaginable ways. The automatic classification of FN is a vital tool to prevent their dissemination and support fact-checking. Related work has shown that FN spread faster, deeper, and more broadly than truthful news on social media. Deep learning has produced state-of-the-art solutions in this field, mainly based on textual attributes. In this paper, we propose to combine compact representations of the textual news properties generated using DistilBERT, with topological metrics extracted from their propagation network in social media. Using a dataset related to politics and distinct learning algorithms, we extensively assessed the components of the proposed solution. Regarding the textual attributes, we reached results comparable to stateof-the-art solutions using only the news title and contents, which is useful for FN early detection. We assessed the influential topological metrics, and the effect of their combination with the news textual features. We also explored the use of ensembles. Our results were very promising, revealing the potential of the features proposed and the adoption of ensembles.

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