Overcoming Information Overload in the Enterprise: The Active Approach

Simperl, Elena; Thurlow, Ian; Warren, Paul; Dengler, Frank; Davies, John; Grobelnik, Marko; Mladenić, Dunja; Gómez-Pérez, José Manuel; Moreno, Carlos Ruiz

doi:10.1109/mic.2010.146

Cited by 24 publications

(8 citation statements)

References 5 publications

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“…''Information overload'' refers to the variety and quantity of stimuli that exceeds the receiver's ability to integrate and process them (Jackson & Farzaneh, 2012;Jacoby, 1977;McCormick, 1970). With the increasing use of information technology, the overabundance of information has become a bigger issue than lack of information (Simperl et al, 2010). Information overload impairs comprehension (Hildon, Allwood, & Black, 2012;Lipowski, 1975) and hampers performance (Driver & Mock, 1975;O'Reilly, 1980;Jakoby, Speller, & Kohn, 1974;Schultz, Schreyoegg, & von Reitzenstein, 2013).…”

Section: Expertise Of Twitter Followersmentioning

confidence: 99%

A study of factors that contribute to online review helpfulness

Huang

Chen

Yen

et al. 2015

Computers in Human Behavior

298

227

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Section: Expertise Of Twitter Followersmentioning

confidence: 99%

A study of factors that contribute to online review helpfulness

Huang

Chen

Yen

et al. 2015

Computers in Human Behavior

298

227

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“…Thus, those online reviews can differ easily by the experience obtained in the perspective of the information. So, images and videos may not be significantly helpful because they cannot give to the reader the complement of the information seeking, and for this type of good because of the increment of information technology, the overabundance of information has been more problematic than the lack of information [47]. Furthermore, due to there are different opinions of the buyers when writing reviews, the consumers can find ORs which are much longer, well expressed, clearly and usually contradictory more helpful than ORs that are short, where these short ORs can be mostly found in the category of Search goods [10] and now more often showing images and/or videos.…”

Section: Research Model and Hypothesismentioning

confidence: 99%

Difference Analysis and Prediction of the Helpfulness in Online Reviews of Experience-based, Search-based, and Mixed-based Goods

C.¹

2022

JMHR

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Online reviews (ORs) have shown evidence to help consumers to reduce hesitation in the last stage of the purchase and has been also found that ORs help online businesses increase sales. However, ORs are increasing faster, becoming every time more robust with better ways and media to express useful and helpful information. Therefore, the way ORs help online business and consumers are constantly changing. Previous studies have intended to analyze helpfulness in different ways. However, they have not totally yet identified the most appropriate influence significance of the factors to test and predict the helpfulness of ORs due to the constant change and evolution of ORs in E-commerce platforms. I based this study on the economics of information, media richness, and negativity-bias theories, proposing a model that shows the influencing factors in the helpfulness of ORs (such as length, sentimental Analysis, score rating, number of images, video and published days). To find a closer optimal helpfulness analysis and prediction, a data set of 17,119 samples of three types of online goods have been extracted from different products on Amazon.com. For the analysis, we have considered employing a regression model to analyze the significance level of the factors in ORs for every type of online goods. The findings in this research prove that in fact there is a different perception of helpfulness for every type of good.

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“…More generally, our work relates to projects on developing intelligent virtual meeting assistants [15,16], as we see dealing with information overload as an important capability of such agents. Even more broadly, there has been some research outside the meetings domain on using machine learning and artificial intelligence to help alleviate information overload in different application areas [17][18][19].…”

Section: Related Workmentioning

confidence: 99%

Information Processing and Overload in Group Conversation: A Graph-Based Prediction Model

Murray

2019

MTI

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Based on analyzing verbal and nonverbal features of small group conversations in a task-based scenario, this work focuses on automatic detection of group member perceptions about how well they are making use of available information, and whether they are experiencing information overload. Both the verbal and nonverbal features are derived from graph-based social network representations of the group interaction. For the task of predicting the information use ratings, a predictive model using random forests with verbal and nonverbal features significantly outperforms baselines in which the mean or median values of the training data are predicted, as well as significantly outperforming a linear regression baseline. For the task of predicting information overload ratings, the multimodal random forests model again outperforms all other models, including significant improvement over linear regression and gradient boosting models. However, on that task the best model is not significantly better than the mean and median baselines. For both tasks, we analyze performance using the full multimodal feature set versus using only linguistic features or only turn-taking features. While utilizing the full feature set yields the best performance in terms of mean squared error (MSE), there are no statistically significant differences, and using only linguistic features gives comparable performance. We provide a detailed analysis of the individual features that are most useful for each task. Beyond the immediate prediction tasks, our more general goal is to represent conversational interaction in such a way that yields a small number of features capturing the group interaction in an easily interpretable manner. The proposed approach is relevant to many other group prediction tasks as well, and is distinct from both classical natural language processing (NLP) as well as more current deep learning/artificial neural network approaches.

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Overcoming Information Overload in the Enterprise: The Active Approach

Cited by 24 publications

References 5 publications

A study of factors that contribute to online review helpfulness

A study of factors that contribute to online review helpfulness

Difference Analysis and Prediction of the Helpfulness in Online Reviews of Experience-based, Search-based, and Mixed-based Goods

Information Processing and Overload in Group Conversation: A Graph-Based Prediction Model

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