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The purpose of this paper is to introduce the concept of social relevance assessments, which are judgments made by individuals when they seek out information within virtual social worlds such as online support groups (OSGs).

Constructivist grounded theory was employed to examine the phenomenon of information exchange in OSGs for chronic kidney disease. In-depth interviews were conducted with 12 participants, and their posts in three OSGs were also harvested. Data were analyzed using inductive content analysis and the constant comparative method. Theoretical sampling was conducted until saturation was reached. Member checking, peer debriefing, and triangulation were used to verify results.

There are two levels of relevance assessment that occur when people seek out information in OSGs. First, participants evaluate the OSG to determine whether or not the group is an appropriate place for information exchange about kidney disease. Second, participants evaluate individual users on the OSG to see if they are appropriate people with whom to exchange information. This often takes the form of similarity assessment, whereby people try to determine whether or not they are similar to specific individuals on the forums. They use a variety of heuristics to assess similarity as part of this process.

This paper extends the author’s understanding of relevance in information science in two fundamental ways. Within the context of social information exchange, relevance is socially constructed and is based on social characteristics, such as age, shared beliefs, and experience. Moreover, relevance is assessed both when participants seek out information and when they disclose information, suggesting that the conception of relevance as a process that occurs primarily during information seeking is limited.

The determination of the measurement uncertainty is relevant for all measurement processes. In production engineering, the measurement uncertainty needs to be known to avoid erroneous decisions. However, its determination is associated to high effort due to the expertise and expenditure that is needed for modelling measurement processes. Once a measurement model is developed, it cannot necessarily be used for any other measurement process. In order to make an existing model useable for other measurement processes and thus to reduce the effort for the determination of the measurement uncertainty, a procedure for the migration of measurement models has to be developed.

This paper presents an approach to migrate measurement models from an old process to a new “similar” process. In this approach, the authors first define “similarity” of two processes mathematically and then use it to give a first estimate of the measurement uncertainty of the similar measurement process and develop different learning strategies. A trained machine-learning model is then migrated to a similar measurement process without having to perform an equal size of experiments.Similarity assessment and model migration

The authors’ findings show that the proposed similarity assessment and model migration strategy can be used for reducing the effort for measurement uncertainty determination. They show that their method can be applied to a real pair of similar measurement processes, i.e. two computed tomography scans. It can be shown that, when applying the proposed method, a valid estimation of uncertainty and valid model even when using less data, i.e. less effort, can be built.

The proposed strategy can be applied to any two measurement processes showing a particular “similarity” and thus reduces the effort in estimating measurement uncertainties and finding valid measurement models.

The purpose of this paper is to propose a similarity-based approach to accurately retrieve reference solutions for the intelligent handling of online complaints.

This approach uses a case-based reasoning framework and firstly formalizes existing online complaints and their solutions, new online complaints, and complaint products, problems and content as source cases, target cases and distinctive features of each case, respectively. Then the process of using existing word-level, sense-level and text-level measures to assess the similarities between complaint products, problems and contents is explained. Based on these similarities, a measure with high accuracy in assessing the overall similarity between cases is designed. The effectiveness of the approach is evaluated by numerical and empirical experiments.

The evaluation results show that a measure simultaneously considering the features of similarity at word, sense and text levels can obtain higher accuracy than those measures that consider only one level feature of similarity; and that the designed measure is more accurate than all of its linear combinations.

The approach offers a feasible way to reduce manual intervention in online complaint handling. Complaint products, problems and content should be synthetically considered when handling an online complaint. The designed procedure of the measure with high accuracy can be applied in other applications that consider multiple similarity features or linguistic levels.

A method for linearly combining the similarities at all linguistic levels to accurately assess the overall similarities between online complaint cases is presented. This method is experimentally verified to be helpful to improve the accuracy of online complaint case retrieval. This is the first study that considers the accuracy of the similarity measures for online complaint case retrieval.

The purpose of this paper is to obtain online access to the digitised Arabic manuscripts images, which need to use a catalogue. The bibliographic cataloguing is unsuitable for old Arabic manuscripts, and it is imperative to establish a new cataloguing model. In the research, the authors propose a new cataloguing model based on manuscript annotations and transcriptions. This model can be an effective solution to dynamic catalogue old Arabic manuscripts. In this field, the authors used the automatic extraction of the metadata that is based on the structural similarity of the documents.

This work is based on experimental methodology. The whole proposed concepts and formulas were tested for validation. This, allows the authors to make concise conclusions.

Cataloguing old Arabic manuscripts faces problem of unavailability of information. However, this information may be found in another place in a copy of the original manuscript. Thus, cataloguing Arabic manuscript cannot be done in one time, it is a continual process which require information updating. The idea is to make a pre-cataloguing of a manuscript, then try to complete and improve it through a specific platform. Consequently, in the research work, the authors propose a new cataloguing model, which the authors call “Dynamic cataloguing”.

The success of the proposed model is confronted with the involvement of all actors of the model. It is based on the conviction and the motivation of actors of the collaborative platform.

The model can be used in several cataloguing fields, where the encoding model is based on XML. The model is innovative and implements a smart cataloguing model. The model is useful by using a web platform. It allows an automatic update of a catalogue.

The model prompts the user to participate and enrich the catalogue. The user could improve his social status from a passive to an active.

The dynamic cataloguing model is a new concept. It has never been proposed in the literature until now. The proposed cataloguing model is based on automatic extraction of metadata from user annotations/transcription. It is a smart system which automatically updates or fills the catalogue with the extracted metadata.

The purpose of the paper is to predict mapping of areas vulnerable to flooding in the Ourika watershed in the High Atlas of Morocco with the aim of providing a useful tool capable of helping in the mitigation and management of floods in the associated region, as well as Morocco as a whole.

Four machine learning (ML) algorithms including k-nearest neighbors (KNN), artificial neural network, random forest (RF) and x-gradient boost (XGB) are adopted for modeling. Additionally, 16 predictors divided into categorical and numerical variables are used as inputs for modeling.

The results showed that RF and XGB were the best performing algorithms, with AUC scores of 99.1 and 99.2%, respectively. Conversely, KNN had the lowest predictive power, scoring 94.4%. Overall, the algorithms predicted that over 60% of the watershed was in the very low flood risk class, while the high flood risk class accounted for less than 15% of the area.

There are limited, if not non-existent studies on modeling using AI tools including ML in the region in predictive modeling of flooding, making this study intriguing.

The present study aims to further examine the persuasive effect of pictures in a print ad according to the recipient’s ability to process the information and to observe to what extent the presence of a picture could negatively influence recipients’ attitude toward the ad’s verbal claim.

Two studies were designed to manipulate the presence vs absence of an attractive/unattractive picture, the kind of verbal claims (affectively based vs rationally based) and the recipient’s ability to process the ad (cognitive load vs no cognitive load).

Main findings showed that the presence of an attractive picture elicited an unfavorable attitude toward the functional verbal claim when recipients were not cognitively charged. Furthermore, it proved to be a mediator of the influence of pictures on attitude toward the ad. The positive influence of an attractive picture on product evaluation and purchase intention was greater under a cognitive load but showed contrasting results for price perceptions. For the unattractive picture, cognitive load was found to be a moderator only when recipients had to infer the product price.

The present research emphasized the negative influence of attractive pictures on functional verbal claims and the moderating role of cognitive load on pictorial stimuli either acting as peripheral or central cues in the persuasive process.

Practitioners may want to consider that an attractive picture in advertising is not always the best route for persuasion, especially when the verbal ad content emphasizes the product’s properties.

The present study provides new insights regarding the role of pictures in advertising persuasive effectiveness. Until now, no research had addressed the extent to which the presence of a picture could affect processing of an ad’s verbal claims. Additionally, the present study expands research on persuasive communication and affirms the necessity of more intensively investigating the role of pictures in advertising under the rubric of information processing level.

To elaborate the nature of fact-checking in the domain of political information by examining how fact-checkers assess the validity of claims concerning the Russo-Ukrainian conflict and how they support their assessments by drawing on evidence acquired from diverse sources of information.

Descriptive quantitative and qualitative content analysis of 128 reports written by the fact-checkers of Snopes – an established fact-checking organisation – during the period of 24 February 2022 – 28 June, 2023. For the analysis, nine evaluation grounds were identified, most of them inductively from the empirical material. It was examined how the fact-checkers employed such grounds while assessing the validity of claims and how the assessments were bolstered by evidence acquired from information sources such as newspapers.

Of the 128 reports, the share of assessments indicative of the invalidity of the claims was 54.7%, while the share of positive ratings was 26.7%. The share of mixed assessments was 15.6%. In the fact-checking, two evaluation grounds, that is, the correctness of information and verifiability of an event presented in a claim formed the basis for the assessment. Depending on the topic of the claim, grounds such as temporal and spatial compatibility, as well as comparison by similarity and difference occupied a central role. Most popular sources of information offering evidence for the assessments include statements of government representatives, videos and photographs shared in social media, newspapers and television programmes.

As the study concentrated on fact-checking dealing with political information about a specific issue, the findings cannot be extended to concern the fact-checking practices in other contexts.

The study is among the first to characterise how fact-checkers employ evaluation grounds of diverse kind while assessing the validity of political information.

The purpose of this paper is to explore the influence of five dimensions of similarity (i.e. condition similarity, context similarity, catalyst similarity, consequence similarity and connection similarity) on Facebook social networks development.

A questionnaire-based survey was conducted with 245 Romanian college students. SmartPLS 3 statistical software for partial least squares structural equation modeling was chosen as the most adequate technique for the assessment of models with both composites and reflective constructs.

More than 52 percent of the variance in social network development was explained by the advanced similarity model. Each dimension had a positive effect on Facebook social networks development, the highest influences being exerted by condition similarity, context similarity and consequence similarity.

The current approach is substantively based on the homophily paradigm in explaining social network development. Future research would benefit from comparing and contrasting complementary theories (e.g. the rational self-interest paradigm, the social exchange or dependency theories) with the current findings. Also, the research is tributary to a convenience-based sample of Romanian college students which limits the generalization of the results to other cultural contexts and, thus, invites further research initiatives to test the model in different settings.

Similarity attributes and mechanisms consistently determine the dynamics of online social networks, a fact which should be investigated in depth in terms of the impact of new technologies among young people.

This study is among the first research initiatives to approach similarity structures and processes within an integrative framework and to conduct the empirical analysis beyond US-centric samples.

The purpose of this paper is to integrate research conducted on work values, political values and cultural values to develop a new heuristic model of values that can be applied to workplace outcomes.

This is a conceptual paper that draws upon the work values, political values and cultural values literatures and the “similarity leads to attraction” and “dissimilarity leads to repulsion” hypotheses to advocate an integrative conceptual model spanning these constructs.

Integrating the three types of values with the underlying hypotheses of “similarity leads to attraction” and “dissimilarity leads to repulsion”, an internally consistent two-factor model of values is developed. This heuristic model argues that one set of factors causes value congruence and its associated outcomes and a different set of factors causes value incongruence and its associated outcomes. By conceptualizing value congruence and value incongruence as a two-factor theory, the idea is advanced that people do not assess value similarity and value dissimilarity unidimensionally, but these are two separate dimensions supported by different theoretical hypotheses and processes.

Previous conceptual work on values has isolated different types of values and considered them separately. A contribution is made by integrating the three main streams of values research. The paper is the first to advocate a two-factor theory to values and the first to incorporate the “similarity leads to attraction” and “dissimilarity leads to repulsion” hypotheses. The model repositions the focus for future research on value congruence and incongruence.