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The purpose of this paper is to identify the role of emotional aspects in information retrieval of PhD students from the web.

From the methodological perspective, the present study is experimental and the type of study is practical. The study population is PhD students of various fields of science. The study sample consists of 50 students as selected by the stratified purposive sampling method. The information aggregation is performed by observing the records of user’s facial expressions, log file by Morae software, as well as pre-search and post-search questionnaire. The data analysis is performed by canonical correlation analysis.

The findings showed that there was a significant relationship between emotional expressions and searchers’ individual characteristics. Searchers satisfaction of results, frequency internet search, experience of search, interest in the search task and familiarity with similar searches were correlated with the increased happy emotion. The examination of user’s emotions during searching performance showed that users with happiness emotion dedicated much time in searching and viewing of search solutions. More internet addresses with more queries were used by happy participants; on the other hand, users with anger and disgust emotions had the lowest attempt in search performance to complete search process.

The results imply that the information retrieval systems in the web should identify emotional expressions in a set of perceiving signs in human interaction with computer, similarity, face emotional states, searching and information retrieval from the web.

The results explicit in the automatic identification of users’ emotional expressions can enter new dimensions into their moderator and information retrieval systems on the web and can pave the way of design of emotional information retrieval systems for the successful retrieval of users of the network.

The purpose of this paper is to explore how both producers and consumers of user-created music videos on YouTube communicate emotional information.

In total, 150 filmic documents containing fan-generated versions of U2’s “Song for Someone” were purposively collected. The author used discourse analysis to understand the types of videos created, the communication of emotional information from both the producers and the consumers, the social construction of emotion in the filmic documents, and elements of intertextuality that represented emotion.

Fans created videos containing cover versions, original versions of the song with new visual content, and tutorials about how to play the song. Producers of cover versions communicated emotional information, especially tenderness, through facial expression, their surroundings, and corresponding musical elements. Producers’ visual content expressed emotion through meaningful photographs and sad stories. Producers’ descriptions revealed emotion as well. Emotions were individually experienced and socially constructed. Consumers conveyed emotion through likes, dislikes, and expressive positive comments. Intertextuality communicated passion for U2 through tour references, paraphernalia displays, band photographs, imitating the band, and musical mashups.

Information science can work towards a new generation of multimedia information retrieval systems that incorporate emotion in order to help users discover documents in meaningful ways that move beyond keyword and bibliographic searches.

This is one of the earliest research papers in the area of emotional information retrieval (EmIR).

The object of this empirical research study is emotion, as depicted and aroused in videos. This paper seeks to answer the questions: Are users able to index such emotions consistently? Are the users' votes usable for emotional video retrieval?

The authors worked with a controlled vocabulary for nine basic emotions (love, happiness, fun, surprise, desire, sadness, anger, disgust and fear), a slide control for adjusting the emotions' intensity, and the approach of broad folksonomies. Different users tagged the same videos. The test persons had the task of indexing the emotions of 20 videos (reprocessed clips from YouTube). The authors distinguished between emotions which were depicted in the video and those that were evoked in the user. Data were received from 776 participants and a total of 279,360 slide control values were analyzed.

The consistency of the users' votes is very high; the tag distributions for the particular videos' emotions are stable. The final shape of the distributions will be reached by the tagging activities of only very few users (less than 100). By applying the approach of power tags it is possible to separate the pivotal emotions of every document – if indeed there is any feeling at all.

This paper is one of the first steps in the new research area of emotional information retrieval (EmIR). To the authors' knowledge, it is the first research project into the collective indexing of emotions in videos.

Readers articulate mood in deeply subjective ways, yet the underlying structure of users' understanding of the media they consume has important implications for retrieval and access. User articulations might at first seem too idiosyncratic, but organizing them meaningfully has considerable potential to provide a better searching experience for all involved. The current study develops mood categories inductively for fiction organization and retrieval in information systems.

The authors developed and distributed an open-ended survey to 76 fiction readers to understand their preferences with regard to the affective elements in fiction. From the fiction reader responses, the research team identified 161 mood terms and used them for further categorization.

The inductive approach resulted in 30 categories, including angry, cozy, dark and nostalgic. Results include three overlapping mood families: Emotion, Tone/Narrative, and Atmosphere/Setting, which in turn relate to structures that connect reader-generated data with conceptual frameworks in previous studies.

The inherent complexity of “mood” should not dissuade researchers from carefully investigating users' preferences in this regard. Adding to the existing efforts of classifying moods conducted by experts, the current study presents mood terms provided by actual end-users when describing different moods in fiction. This study offers a useful roadmap for creating taxonomies for retrieval and description, as well as structures derived from user-provided terms that ultimately have the potential to improve user experience.

The purpose of this conceptual paper is to consider the possibility of designing a story-based image indexing system based on users’ descriptions of images. It reports a pilot study which uses users’ descriptions of two images.

Eight interviews were undertaken to investigate storytelling in user interpretations of the images. Following this, storytelling was explored as an indexing input method. In all, 26 research subjects were asked to create stories about the images, which were then considered in relation to conventional story elements and in relation to Hidderley and Rafferty's (2005) image modality model.

The results of the semi-structured interviews revealed that the majority of interpretations incorporated story elements related to setting, character, plot, literary devices, and themes. The 52 image stories included story elements identified in the first part of the project, and suggested that the image modality model is robust enough to deal with the “writerly” images used in this study. In addition, using storytelling as an input method encourages the use of verbs and connotative level responses.

User indexing is generally based on paradigmatic approaches to concept analysis and interpretation in the form of tagging; the novelty of the current study is its exploration of syntagmatic approaches to user indexing in the form of storytelling. It is a pilot, proof of concept study, but it is hoped that it might stimulate further interest in syntagmatic approaches to user indexing.

This study aims to consider the value of user‐assigned image tags by comparing the facets that are represented in image tags with those that are present in image queries to see if there is a similarity in the way that users describe and search for images.

A sample dataset was created by downloading a selection of images and associated tags from Flickr, the online photo‐sharing web site. The tags were categorised using image facets from Shatford's matrix, which has been widely used in previous research into image indexing and retrieval. The facets present in the image tags were then compared with the results of previous research into image queries.

The results reveal that there are broad similarities between the facets present in image tags and queries, with people and objects being the most common facet, followed by location. However, the results also show that there are differences in the level of specificity between tags and queries, with image tags containing more generic terms and image queries consisting of more specific terms. The study concludes that users do describe and search for images using similar image facets, but that measures to close the gap between specific queries and generic tags would improve the value of user tags in indexing image collections.

Research into tagging has tended to focus on textual resources with less research into non‐textual documents. In particular, little research has been undertaken into how user tags compare to the terms used in search queries, particularly in the context of digital images.

Online images can convey sensory-based elements affecting digital users' emotions and digital engagement. The purpose of this study is to investigate which image-based features are more effective in conveying and stimulating particular emotions and engagement towards organizations operating in the food industry.

An online experimental survey was implemented. Two image-based features, narrativity and dynamism were chosen. The stimuli comprise four images, one with high and one with low level of narrativity, and one with high and one with low dynamism, published by a food company on its official Instagram account. Food-identity, emotional appeals and digital visual engagement behaviours were measured. A total of 141 students between 19 and 25 years old of a European University completed the questionnaire. Data was analysed through SPSS software using t-test analysis.

Results show that both narrativity and dynamism impact digital users' emotions and it was found to impact digital visual engagement attitude. Food involvement was measured in terms of food identity impact the effects of specific image-based features on emotions and visual engagement.

The study focuses on only two visual social semiotics features – narrativity and dynamism – and therefore, only partially captures the potentialities of images in digital communications.

This study provides professionals with empirical evidence and insights for effectively planning a visual social media strategy.

This paper contributes to the stream of research in social media communications by investigating the visual social semiotic features of images published online by a food company.

Social question and answer (social Q&A) sites have become a popular tool for obtaining music information. The purpose of this paper is to investigate what users ask about, what experience the questions convey, and how users specify their questions.

A total of 3,897 music questions from the social Q&A site Yahoo! Answers were categorized according to their question type, user experience, and question specification.

The music questions were diverse with (dis)approval (42 percent), factual (21 percent), and advice (15 percent) questions as the most frequent types. Advice questions were the longest and roughly twice as long as (dis)approval and factual questions. The user experience associated with the questions was most often pragmatic (24 percent) or senso-emotional (12 percent). Pragmatic questions were typically about the user’s own performance of music, while senso-emotional questions were about finding music for listening. Notably, half of the questions did not convey information about the user experience but the absence of such information did not reduce the number of answers. In specifying the questions, the most frequent information was about the music context and the user context.

This study suggests a division of labor between social Q&A sites and search engines for music information retrieval. It should be noted that the study is restricted to one social Q&A site.

Social Q&A sites provide an opportunity for studying what information real users seek about music and what information they specify to retrieve it, thereby elucidating the role of social Q&A in music information seeking.

As an emerging tool for data discovery, data retrieval systems fail to effectively support users' cognitive processes during data search and access. To uncover the relationship between data search and access and the cognitive mechanisms underlying this relationship, this paper examines the associations between affective memories, perceived value, search effort and the intention to access data during users' interactions with data retrieval systems.

This study conducted a user experiment for which 48 doctoral students from different disciplines were recruited. The authors collected search logs, screen recordings, questionnaires and eye movement data during the interactive data search. Multiple linear regression was used to test the hypotheses.

The results indicate that positive affective memories positively affect perceived value, while the effects of negative affective memories on perceived value are nonsignificant. Utility value positively affects search effort, while attainment value negatively affects search effort. Moreover, search effort partially positively affects the intention to access data, and it serves a full mediating role in the effects of utility value and attainment value on the intention to access data.

Through the comparison between the findings of this study and relevant findings in information search studies, this paper reveals the specificity of behaviour and cognitive processes during data search and access and the special characteristics of data discovery tasks. It sheds light on the inhibiting effect of attainment value and the motivating effect of utility value on data search and the intention to access data. Moreover, this paper provides new insights into the role of memory bias in the relationships between affective memories and data searchers' perceived value.

The purpose of this paper is to understand blog users’ negative emotional norm compliance decision-making in crises (blog users’-NNDC).

A belief–desire–intention (BDI) model to evaluate the blog users’-NNDC (the BDI-NNDC model) was developed. This model was based on three social characteristics: self-interests, expectations and emotions. An experimental study was conducted to evaluate the efficiency of the BDI-NNDC model by using data retrieved from a popular Chinese social network called “Sina Weibo” about three major crises.

The BDI-NNDC model strongly predicted the Blog users’-NNDC. The predictions were as follows: a self-interested blog user posted content that was targeting his own interests; a blogger with high expectations wrote and commented emotionally negative blogs on the condition that the numbers of negative posts increased, while he ignored the norm when there was relatively less negative emotional news; and an emotional blog user obeyed the norm based on the emotional intentions of the blogosphere in most of the cases.

The BDI-NNDC model can explain the diffusion of negative emotions by blog users during crises, and this paper shows a way to bridge the social norm modelling and the research of blog users’ activity and behaviour characteristics in the context of “real life” crises. However, the criterion for differentiating blog users according to social characteristics needs to be further revised, as the generalizability of the results is limited by the number of cases selected in this study.

The current method could be applied to predict emotional trends of blog users who have different social characteristics and it could support government agencies to build strategic responses to crises.

This paper supports the creation of normative models and engineering methods to predict blog users’-NNDC and mitigate their effect in real-world crises.