Blogger-centric contextual advertising

Fan, Teng-Kai; Chang, Chia‐Hui

doi:10.1145/1645953.1646234

Cited by 7 publications

(4 citation statements)

References 10 publications

(13 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Advertising agencies look at what type of information bloggers are posting as well as visitor traffic. Relevant ads should not be based on geographical location but rather websites visited, because around 80 percent of internet users have an interest in personalized ad content when they visit websites they are attracted to (Fan and Chang, 2011). Affiliate companies allow bloggers to place banners, text links and image links to products or websites that consumers click on.…”

Section: Literature Reviewmentioning

confidence: 99%

Consumer attitudes towards bloggers and paid blog advertisements: what’s new?

Esch

Arli

Castner

et al. 2018

MIP

View full text Add to dashboard Cite

Purpose Reports show that 6.77m people published blogs on blogging websites and more than 12m people write blogs using their social network. However, few studies have explored consumer attitudes toward bloggers and their advertising. Therefore, an effort to discover how paid blog advertisements influence consumer attitudes toward bloggers and the products they advertise will help marketers gain an understanding of how to use bloggers as paid sponsors to influence consumer purchase intent. Using online survey approach, a study recruited participants (n=210) through an online survey platform (MTurk). The results indicated that the similarity between the consumer and the ad creator is an important psychological reason why consumers are more likely to perceive advertisements as more authentic, more affective, less deceptive and more credible, and they are thus more likely to trust the blogger. On the other hand, the importance of ad attribute (authenticity) does not significantly influence consumers’ intention to purchase products advertised by a blogger. As consumers are becoming more skeptical of advertisements, blogs need to be entertaining and a pleasure to view. Not only does the content need to be interesting, the design, flow and clarity of the blogs must also be considered important factors. In addition, advertising needs to be believable, credible and honest. In the online world, trust and credibility are still paramount in attracting consumers’ engagement; hence, promoting companies’ products and services through blogs can be an effective strategy to lower consumer skepticism barriers. The paper aims to discuss these issues. Design/methodology/approach Participants answered questions about their attitudes toward bloggers and their advertising when purchasing products in an online retail environment. Furthermore, participants also answered questions about their perceived trust in the bloggers themselves as well as the authenticity and credibility of the brand-related communication received from bloggers. Moreover, they reported on their attitudes toward how deceptive they considered bloggers and their advertising to be. Finally, participants reported how paid blog advertisements influence their purchase intent. Findings Similarity toward the ad creator is an important psychological reason behind consumers’ attitude toward blogs. Consumers who follow a blog often have the same interests and are thus more likely to support bloggers. This idea can be used as a segmentation strategy to reach particular consumers. Consumers who perceive similarity with the ad creator are more likely to recognize the ads as more authentic, affective, credible and trustworthy as well as less deceptive, regarding the blogger. Blogs need to be entertaining and a pleasure to view. Not only does the content need to be interesting but the design, flow and clarity of the blog are also important factors. Blog advertising needs to be believable, credible and honest. In the online world, trust and credibility are still paramount in attracting consumers’ engagement with the website, bloggers or social media. Originality/value Trust in the blogger did not influence consumers’ purchase intent; however, it did influence their attitude toward how similar they are with the blogger. In an online shopping environment, the human touch and personal contact between consumers and retailers has been lost. Consumers often leave the online transaction due to a lack of trust. Therefore, bloggers can be replacements for the missing “salesperson” in online interactions. Companies can use bloggers as the mediating person to reach their intended audiences, bridging the gap between the company and consumers.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Consumer attitudes towards bloggers and paid blog advertisements: what’s new?

Esch

Arli

Castner

et al. 2018

MIP

View full text Add to dashboard Cite

show abstract

“…Deep learning approaches have gained immense popularity over machine learning algorithms [ 38 – 40 ]. This is because deep learning approaches provide reliable results regarding text classification.…”

Section: Related Workmentioning

confidence: 99%

Improving Sentiment Analysis for Social Media Applications Using an Ensemble Deep Learning Language Model

Alsayat

2021

Arab J Sci Eng

View full text Add to dashboard Cite

As data grow rapidly on social media by users’ contributions, specially with the recent coronavirus pandemic, the need to acquire knowledge of their behaviors is in high demand. The opinions behind posts on the pandemic are the scope of the tested dataset in this study. Finding the most suitable classification algorithms for this kind of data is challenging. Within this context, models of deep learning for sentiment analysis can introduce detailed representation capabilities and enhanced performance compared to existing feature-based techniques. In this paper, we focus on enhancing the performance of sentiment classification using a customized deep learning model with an advanced word embedding technique and create a long short-term memory (LSTM) network. Furthermore, we propose an ensemble model that combines our baseline classifier with other state-of-the-art classifiers used for sentiment analysis. The contributions of this paper are twofold. (1) We establish a robust framework based on word embedding and an LSTM network that learns the contextual relations among words and understands unseen or rare words in relatively emerging situations such as the coronavirus pandemic by recognizing suffixes and prefixes from training data. (2) We capture and utilize the significant differences in state-of-the-art methods by proposing a hybrid ensemble model for sentiment analysis. We conduct several experiments using our own Twitter coronavirus hashtag dataset as well as public review datasets from Amazon and Yelp. For concluding results, a statistical study is carried out indicating that the performance of these proposed models surpasses other models in terms of classification accuracy.

show abstract

“…Using the content-based approach, Mooney and Roy [ 15 ] examines the degree of similarity between consumers and product content profiles, examining parsable information, such as textual data. Mining textual features of user-generated content on SNS, largely Twitter, have been frequently used to predict and target audiences [ 10 ], as well as the discovery of blogger’s personal interests [ 16 ] and content-based recommender system for SNS users with relevant interests and preferences [ 17 ]. Keyword extraction from online content can find meaningful language patterns [ 18 ], and when consumer information is not available, audience attributes such as age, gender and personality are detected with machine learning classifiers [ 19 ].…”

Section: Related Workmentioning

confidence: 99%

Enhancing Personalized Ads Using Interest Category Classification of SNS Users Based on Deep Neural Networks

Hong

Choi

Lim

et al. 2020

Sensors

View full text Add to dashboard Cite

The classification and recommendation system for identifying social networking site (SNS) users’ interests plays a critical role in various industries, particularly advertising. Personalized advertisements help brands stand out from the clutter of online advertisements while enhancing relevance to consumers to generate favorable responses. Although most user interest classification studies have focused on textual data, the combined analysis of images and texts on user-generated posts can more precisely predict a consumer’s interests. Therefore, this research classifies SNS users’ interests by utilizing both texts and images. Consumers’ interests were defined using the Curlie directory, and various convolutional neural network (CNN)-based models and recurrent neural network (RNN)-based models were tested for our user interest classification system. In our hybrid neural network (NN) model, CNN-based classification models were used to classify images from users’ SNS postings while RNN-based classification models were used to classify textual data. The results of our extensive experiments show that the classification of users’ interests performed best when using texts and images together, at 96.55%, versus texts only, 41.38%, or images only, 93.1%. Our proposed system provides insights into personalized SNS advertising research and informs marketers on making (1) interest-based recommendations, (2) ranked-order recommendations, and (3) real-time recommendations.

show abstract

Blogger-centric contextual advertising

Cited by 7 publications

References 10 publications

Consumer attitudes towards bloggers and paid blog advertisements: what’s new?

Consumer attitudes towards bloggers and paid blog advertisements: what’s new?

Improving Sentiment Analysis for Social Media Applications Using an Ensemble Deep Learning Language Model

Enhancing Personalized Ads Using Interest Category Classification of SNS Users Based on Deep Neural Networks

Contact Info

Product

Resources

About