Sociolect-based community detection

Reynolds, W. N.; Salter, William J.; Farber, Robert M.; Corley, Courtney D.; Dowling, Chase; Beeman, William O; Smith‐Lovin, Lynn; Choih, Joon Nak

doi:10.1109/isi.2013.6578823

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…They then use the notion of network centrality to assess the relative importance of network members. Recently, Reynolds et al (2013) developed several measures to look for signatures of rhetoric used by groups. These measures exploit linkage patterns in networks constructed by filtering billions of tweets based on various term lists (Fortunato, 2010;Newman, 2003;Porter, Onnela, & Mucha, 2009).…”

Section: Detecting and Forecasting Behavioral Patterns In Social Medimentioning

confidence: 99%

Sociocultural Behavior Sensemaking: State of the Art in Understanding the Operational Environment

Egeth¹,

Klein²,

Schmorrow³

2014

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Section: Detecting and Forecasting Behavioral Patterns In Social Medimentioning

confidence: 99%

Sociocultural Behavior Sensemaking: State of the Art in Understanding the Operational Environment

Egeth¹,

Klein²,

Schmorrow³

2014

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“…It is known that the language used in social media is described as a dialect, and in fact, the variation of social media language is aligned with social factors such as geography and ethnicity. Likewise, sociolects are specialized vocabularies used by social subgroups defined by common interests or origins [9,10]. They actually constitute language use similarities characterizing groups of individuals [11].…”

Section: Introductionmentioning

confidence: 99%

Detection of Sociolinguistic Features in Digital Social Networks for the Detection of Communities

et al. 2021

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The emergence of digital social networks has transformed society, social groups, and institutions in terms of the communication and expression of their opinions. Determining how language variations allow the detection of communities, together with the relevance of specific vocabulary (proposed by the National Council of Accreditation of Colombia (Consejo Nacional de Acreditación -CNA) to determine the quality evaluation parameters for universities in Colombia) in digital assemblages could lead to a better understanding of their dynamics and social foundations, thus resulting in better communication policies and intervention where necessary. The approach presented in this paper intends to determine what are the semantic spaces (sociolinguistic features) shared by social groups in digital social networks. It includes five layers based on Design Science Research, which are integrated with Natural Language Processing techniques (NLP), Computational Linguistics (CL), and Artificial Intelligence (AI). The approach is validated through a case study wherein the semantic values of a series of "Twitter" institutional accounts belonging to Colombian Universities are analyzed in terms of the 12 quality factors established by CNA. In addition, the topics and the sociolect used by different actors in the university communities are also analyzed. The current approach allows determining the sociolinguistic features of social groups in digital social networks. Its application allows detecting the words or concepts to which each actor of a social group (university) gives more importance in terms of vocabulary.

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Celebrity profiling through linguistic analysis of digital social networks

2021

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Digital social networks have become an essential source of information because celebrities use them to share their opinions, ideas, thoughts, and feelings. This makes digital social networks one of the preferred means for celebrities to promote themselves and attract new followers. This paper proposes a model of feature selection for the classification of celebrities profiles based on their use of a digital social network Twitter. The model includes the analysis of lexical, syntactic, symbolic, participation, and complementary information features of the posts of celebrities to estimate, based on these, their demographic and influence characteristics. The classification with these new features has an F1-score of 0.65 in Fame, 0.88 in Gender, 0.37 in Birth year, and 0.57 in Occupation. With these new features, the average accuracy improve up to 0.14 more. As a result, extracted features from linguistic cues improved the performance of predictive models of Fame and Gender and facilitate explanations of the model results. Particularly, the use of the third person singular was highly predictive in the model of Fame.

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Sociolect-based community detection

Cited by 3 publications

References 21 publications

Sociocultural Behavior Sensemaking: State of the Art in Understanding the Operational Environment

Sociocultural Behavior Sensemaking: State of the Art in Understanding the Operational Environment

Detection of Sociolinguistic Features in Digital Social Networks for the Detection of Communities

Celebrity profiling through linguistic analysis of digital social networks

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