Exploring Twitter communication dynamics with evolving community analysis

Konstantinidis, Konstantinos T.; Papadopoulos, Symeon; Kompatsiaris, Ioannis

doi:10.7717/peerj-cs.107

Cited by 9 publications

(9 citation statements)

References 41 publications

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“…Word valence and emotional profiles add more emotional contextual information about the stance reconstructed by conceptual associations (Stella, 2020). Although large-scale datasets about sentiment and emotions have only been recently made available to the scientific community by cognitive studies (Warriner, Kuperman & Brysbaert, 2013), they have quickly become predominant in predicting a wide variety of human behaviour (Li, Baucom & Georgiou, 2020) and information processing patterns such as consensus formation in social networks (Konstantinidis, Papadopoulos & Kompatsiaris, 2017) or information sharing on microblogs (Ferrara & Yang, 2015). The combination of network patterns and sentiment data is important, as considering only the frequency of sentiment-labelled content in short texts has been reported to lack interpretative contextual power for estimating how people really feel about a given topic (Beasley & Mason, 2015).…”

Section: Literature Review On Relevant Past Approachesmentioning

confidence: 99%

Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social media

Stella¹

2020

PeerJ Computer Science

103

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Mindset reconstruction maps how individuals structure and perceive knowledge, a map unfolded here by investigating language and its cognitive reflection in the human mind, i.e., the mental lexicon. Textual forma mentis networks (TFMN) are glass boxes introduced for extracting and understanding mindsets’ structure (in Latin forma mentis) from textual data. Combining network science, psycholinguistics and Big Data, TFMNs successfully identified relevant concepts in benchmark texts, without supervision. Once validated, TFMNs were applied to the case study of distorted mindsets about the gender gap in science. Focusing on social media, this work analysed 10,000 tweets mostly representing individuals’ opinions at the beginning of posts. “Gender” and “gap” elicited a mostly positive, trustful and joyous perception, with semantic associates that: celebrated successful female scientists, related gender gap to wage differences, and hoped for a future resolution. The perception of “woman” highlighted jargon of sexual harassment and stereotype threat (a form of implicit cognitive bias) about women in science “sacrificing personal skills for success”. The semantic frame of “man” highlighted awareness of the myth of male superiority in science. No anger was detected around “person”, suggesting that tweets got less tense around genderless terms. No stereotypical perception of “scientist” was identified online, differently from real-world surveys. This analysis thus identified that Twitter discourse mostly starting conversations promoted a majorly stereotype-free, positive/trustful perception of gender disparity, aimed at closing the gap. Hence, future monitoring against discriminating language should focus on other parts of conversations like users’ replies. TFMNs enable new ways for monitoring collective online mindsets, offering data-informed ground for policy making.

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Section: Literature Review On Relevant Past Approachesmentioning

confidence: 99%

Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social media

Stella¹

2020

PeerJ Computer Science

103

View full text Add to dashboard Cite

show abstract

“…Word valence and emotional profiles add more emotional contextual information about the stance reconstructed by conceptual associations (Stella, 2020). Although large-scale datasets about sentiment and emotions have only been recently made available to the scientific community by cognitive studies (Warriner et al, 2013), they have quickly become predominant in predicting a wide variety of human behaviour (Li et al, 2020) and information processing patterns such as consensus formation in social networks (Konstantinidis et al, 2017) or information sharing on microblogs (Ferrara and Yang, 2015). The combination of network patterns and sentiment data is important, as considering only the frequency of sentiment-labelled content in short texts has been reported to lack interpretative contextual power for estimating how people really feel about a given topic (Beasley and Mason, 2015).…”

Section: /25mentioning

confidence: 99%

“…Words are clustered in communities obtained via the Louvain algorithm (cf. (Konstantinidis et al, 2017)). Words in the same community of "woman" in A are plotted through a hierarchical edge-bundling visualisation in C. Positive (negative) words and links are highlighted in cyan (red).…”

Section: /25mentioning

confidence: 99%

Peer Review #1 of "Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social media (v0.2)"

2020

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Mindset reconstruction maps how individuals structure and perceive knowledge, a map unfolded here by investigating language and its cognitive reflection in the human mind, i.e. the mental lexicon. Textual forma mentis networks (TFMN) are glass boxes introduced for extracting and understanding mindsets' structure (in Latin forma mentis) from textual data. Combining network science, psycholinguistics and Big Data, TFMNs successfully identified relevant concepts in benchmark texts, without supervision. Once validated, TFMNs were applied to the case study of distorted mindsets about the gender gap in science. Focusing on social media, this work analysed 10,000 tweets mostly representing individuals' opinions at the beginning of posts. "Gender" and "gap" elicited a mostly positive, trustful and joyous perception, with semantic associates that: celebrated successful female scientists, related gender gap to wage differences, and hoped for a future resolution. The perception of "woman" highlighted jargon of sexual harassment and stereotype threat (a form of implicit cognitive bias) about women in science "sacrificing personal skills for success". The semantic frame of "man" highlighted awareness of the myth of male superiority in science. No anger was detected around "person", suggesting that tweets got less tense around genderless terms. No stereotypical perception of "scientist" was identified online, differently from real-world surveys. This analysis thus identified the Twitter discourse mostly starting conversations as promoting a majorly stereotype-free, positive/trustful perception of gender disparity, aimed at closing the gap. Hence, future monitoring against discriminating language should focus on other parts of conversations like users' replies. TFMNs enable new ways for monitoring collective online mindsets, offering data-informed ground for policy making.

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“…Twitter is currently one of the largest OSN platforms, with 313 million monthly active users [5]. Research also records about 4.4 billion Internet users and there are about 3.4 billion active social media accounts [6].…”

Section: Literature Reviewmentioning

confidence: 99%

Improved Model for Detecting Fake Profiles in Online Social Network: A Case Study of Twitter

Ojo

2019

JAMCS

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Online Social Network (OSN) is like a virtual community where people build social networks and relations with one another. The open access to the Internet has increased the growth of OSN which has attracted intruders to exploit the weaknesses of the Internet and OSN to their own gain. The rise in the usage of OSN has posed security threats to OSN users as they share personal and sensitive information online which could be exploited by these intruders by creating profiles to carry out a series of malicious activities on the social network. In fact, it is no gain saying that the intent of creating fake accounts has adverse effect and the Internet has made it quite easy to concede one’s identity; and this makes it difficult to detect fake accounts as they try to imitate real accounts. In this study, a model that can accurately identify fake profiles in OSN which uses Natural Language Processing Technique to eliminate or reduce the size of the dataset thereby improving the overall performance of the model was proposed. Principal Component Analysis was used for appropriate feature selection. After extraction, six attributes/features that influenced the classifier were found. Support Vector Machine (SVM), Naïve Bayes and Improved Support Vector Machine (ISVM) were used as Classifiers. ISVM introduced a penalty parameter to the standard SVM objective function to reduce the inequality constraints between the slack variables. This gave a better result of 90% than the SVM and Naïve Bayes which gave 77.4% and 77.3% respectively.

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Exploring Twitter communication dynamics with evolving community analysis

Cited by 9 publications

References 41 publications

Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social media

Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social media

Peer Review #1 of "Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social media (v0.2)"

Improved Model for Detecting Fake Profiles in Online Social Network: A Case Study of Twitter

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