Use of microblogging platform for digital communication in politics

Paul, Justin; Parameswar, Nakul; Sindhani, Mohit; Dhir, Sanjay

doi:10.1016/j.jbusres.2021.01.046

Cited by 11 publications

(2 citation statements)

References 67 publications

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“…This level of use has dramatically influenced the way in which consumers interact (Baboo et al, 2022) and has facilitated relationships wherein some specific SM users are exerting a significant influence over others (Thu Bui, 2022). This has led to the emergence of social media influencers (SMIs) in various domains (Paul et al, 2021), who have become part of brands' arsenal to target consumers. This practice is called influencer marketing and has varying definitions, though they converge on a common theme: using people on SM who have developed large followings to influence the attitudes, opinions, and behaviors of other consumers.…”

Section: Introductionmentioning

confidence: 99%

Response to social media influencers: Consumer dispositions as drivers

Bailey

Mishra

Vaishnav

2023

Int J Consumer Studies

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Brand managers use social media influencers (SMIs) to influence consumers' attitudes and intentions. However, there is still ambiguity about the mechanisms by which SMIs influence their followers, as well as the metrics by which their performance can be evaluated. The existing literature has restricted itself to consumers' purchase intentions in order to evaluate SMIs influence and is silent on the role of the consumers' individual characteristics in SMIs influence mechanisms. Based on these research gaps and to provide a clearer picture of the SMI influence mechanism to practitioners, the present study assesses the role of consumer characteristics such as their social media attachment (SMA), parasocial relationships with social media influencer (PSR‐SMIs), and susceptibility to SMIs influence (SSMII) in driving consumer response to SMIs influence through SMI compliance intentions. Study 1 found the sequential mediational role of PSR‐SMIs and SSMII in the SMA‐SMI compliance intentions relationship. Study 2 found the same sequential mediation and demonstrated how SMI skepticism negatively moderates the relationship between PSR‐SMIs and SMI compliance intentions. To the best of the authors' knowledge, this is the first time that this skepticism variable is being explored in the SMI literature.

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Section: Introductionmentioning

confidence: 99%

Response to social media influencers: Consumer dispositions as drivers

Bailey

Mishra

Vaishnav

2023

Int J Consumer Studies

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“…Microblogging websites are valuable data sources that have been effectively used to analyze sociopragmatic phenomena including faith, comment, and emotions in a digital environment. Microblogs are the ones to report breaking news before it reaches corporate media channels [1]. Twitter is one of the most important platforms for investigating Natural Language Processing (NLP).…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Word Embedding with Ensemble Deep Learning for Twitter Rumor Identification

Rajasekar¹,

Robert²

2022

RIA

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In social networks, rumor identification is a major problem. The structural data in a topic is applied to derive useful attributes for rumor identification. Most standard rumor identification methods concentrate on local structural attributes, ignoring the global structural attributes that exist between the source tweet and its responses. To tackle this issue, a Source-Replies relation Graph (SR-graph) has been built to develop an Ensemble Graph Convolutional neural Net (EGCN) with a Nodes Proportion Allocation Mechanism (NPAM) which identifies the rumor. But, the word vectors were trained by the standard word-embedding model which does not increase the accuracy for large Twitter databases. To solve this problem, an unsupervised word-embedding method is needed for large Twitter corpora. As a result, the Twitter word-embedded EGCN (T-EGCN) model is proposed in this article, which uses unsupervised learning-based word embedding to find rumors in huge Twitter databases. Initially, the latent contextual semantic correlation and co-occurrence statistical attributes among words in tweets are extracted. Then, to create a rumor attribute vector of tweets, these word embeddings are concatenated with the GloVe model's word attribute vectors, Twitter-specific attributes, and n-gram attributes. Further, the EGCN is trained by using this attribute vector to identify rumors in a huge Twitter database. Finally, the testing results exhibit that the T-EGCN achieves 87.56% accuracy, whereas the RNN, GCN, PGNN, EGCN, and BiLSTM-CNN attain 65.38%, 68.41%, 75.04%, 81.87%, and 86.12%, respectively for rumor identification.

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Digital Media

Matusitz,

Wesley

2024

Jihad in Sub-Saharan Africa

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Use of microblogging platform for digital communication in politics

Cited by 11 publications

References 67 publications

Response to social media influencers: Consumer dispositions as drivers

Response to social media influencers: Consumer dispositions as drivers

Unsupervised Word Embedding with Ensemble Deep Learning for Twitter Rumor Identification

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