Character Builder Media Semantics [FULL Version] Download
Takeshi Krueger
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The presented mappings are uni-directional mappings, because the semantics of the elements being mapped from the same Media Ontology property may be very different across formats. For example, copyright is mapped to both xmpDM:copyright and dc:rights (as part of the XMP standard [XMP]); the same property is mapped to exif:Copyright (see EXIF [EXIF]). Unfortunately, no semantic relationship can be inferred between the elements defined in the XMP and EXIF standards. The mappings that have been taken into account have different semantics that have one of the following four characteristics:
Related: the two properties are related in a way that is relevant for some use cases, but this relation has no defined and/or commonly applied semantics. For example, in Media RSS [Media RSS], media:credit is related to creator.
The fundamental idea of hypermedia is to enrich the representation of a resource with hypermedia elements.The simplest form of that are links.They indicate a client that it can navigate to a certain resource.The semantics of a related resource are defined in a so-called link relation.You might have seen this in the header of an HTML file already:
An ALPS document can be used as a profile toexplain the application semantics of a document with an application-agnostic media type (such as HTML, HAL, Collection+JSON, Siren,etc.). This increases the reusability of profile documents acrossmedia types.
Large-scale multimedia data are usually strongly correlated in terms of high-level semantics. Cross-media data refers to those data that express semantically similar content but appear in different modalities, different sources, different contexts, etc. Cross-media data are usually characterized by cross-modality, cross-source, and cross-space. Cross-modality means that cross-media data express the same concept, the same semantics, or the same event through different modalities such as image, video, and text. Cross-source refers to cross-media data from other sources but expresses similar semantics [4]. Cross-space refers to the data that coexist in information space and the physical world with the help of interaction behavior mechanisms. Taking advantage of the correlation between cross-media data and digging deeper into the semantics of the information contained in the data is vital for people's travel, life, and society's development. These data have correlation and multimedia characteristics, which reveal the mechanism of coupling mapping and mutual influence between information space, physical world, and human society under the interaction of human information, and realize the crossing of information space and physical space [5]. Taking advantage of the correlation between cross-media data and deeply mining the information semantics contained in the data are of great significance to people's travel, life, and the development of society. By analyzing cross-media data such as pictures and comments in social networks, we can effectively control some current hot tourist destinations and predict the problems affecting tourism safety, which can help tourism management departments or related organizations to formulate corresponding response strategies. By analyzing, learning, and recognizing the semantics of these cross-media data in the tourism field, we can effectively monitor the safety of tourism scenes. Semantic learning based on cross-media tourism data for identification and monitoring effectively achieves tourism safety.
In contrast, the query flow graph model is more affected by the scale of the observed dataset and sensitive to the scale of the experimental dataset. Large-scale multimedia data are usually strongly correlated in high-level semantics. Cross-media data express semantically similar content but appear in different modalities, sources, contexts, etc. It is possible that as the data increases, the performance of the adaptive query recommendation model in this paper will converge with that of the query flow graph model and remain stable (this hypothesis was not verified in this experiment due to the lack of sufficient data). The adaptive query recommendation model (AQR) outperforms the query flow graph model (QFG) in n DCG@10 evaluation metrics, and the possible reasons for this are as follows: (1) This model can select more query terms as candidate recommendation terms by exact matching and fuzzy matching, which enriches the set of candidate query terms. (2) This model can more accurately recommend candidate query terms for users by mining user search logs and integrating user satisfaction with current search results. The results of the AQR and QFG experiments are shown in Figure 8.
The value of a $select System Query Option is a comma-separated list of selection clauses. Each selection clause may be a Property name, Navigation Property name, or the "*" character. The following set of examples uses the data sample data model available at $metadata to describe the semantics for a base set of URIs using the $select system query option. From these base cases, the semantics of longer URIs are defined by composing the rules below.
Content characteristics. In a communication process, content usually refers to a message that the sender directs towards potential receivers. A way to further break down the structure of a communication process, which was also used in the context of research on social media before (e.g., [29]), was proposed by Lasswell [31] who stated that the answers to the following questions represent the main characteristics of an act of communication: (1) who, (2) says what, (3) in which channel, (4) to whom, (5) with what effect?
Content characteristics. To synthesize previous literature, we used the categories listed below for classification (see online appendix for details). To develop this classification scheme, we used previous research that has already introduced related categories such as topic, length or interactivity (e.g., [47]). It has to be noted that the topic category in particular comprises a broad set of characteristics, which led us to split it up further. The main topic category, includes the type of content such as announcements (e.g., [12]), promotions (e.g., [47]) or messages with emotional (e.g., [35]), functional or informational message appeal (e.g., [53]). The component category includes additional elements that are used to, for example, increase media richness, not necessarily related to a certain topic (e.g., video, picture). In the overview of our classification results (see online appendix for details), for each content characteristic we highlight the publications which included at least one indicator to measure them.
An initial result of our literature review is the distribution of reviewed studies related to the relationships that we have investigated, which indicates the relative interest in our two research questions and the respective sub-research questions (Fig. 1). Out of our 45 reviewed studies, 39 investigated the content/engagement relationship (RQ 1), with 13 of them including emotion as a content characteristic (e.g., sentiment of a social media post), though not measuring it on the individual level. Six studies focused on the impact of emotional responses in this category and particularly on the effects of content characteristics on emotional responses (RQ 2a). The link between emotional responses and content engagement (RQ 2b) has only been investigated in two studies. Only one study investigated the mediating effect of emotion (RQ 2c). Overall, the studies focused mainly on the social media platform Facebook (31) and considered hardly any other medium (Twitter (7), YouTube (3), Instagram (1), Weibo (1), Taobao (1), Groupon (1)). Four studies mentioned no specific focus at all. Please note that the assignment of studies to research questions and medium is overlapping and not mutually exclusive.
Evidence for the mediating effect of emotional responses (RQ 2c). Although there has been individual evidence for the relationship between content characteristics and emotional responses (RQ 2a) and emotional responses and engagement behaviors (RQ 2b), we only identified one study in our sample that included indicators for all three areas (i.e., [64]). In this study, Yu evaluated the emotional response based on a content analysis where the emotional perception of content was rated in terms of emotional arousal and valence using two six-item semantic differential scales. They found that social content (e.g., greetings, wishes or questions) is more arousing and positively valenced than promotional content (e.g., advertising, sales promotion or business information). Consequently, users who are more pleased and aroused would rather engage with the content. Yet, in this study a mediating effect was not statistically evaluated, which points to an existing research gap.
RQ 2a. Which effects do content characteristics have on emotions? The main findings related to this question result from the analysis of the emotional responses to video content (4 studies). All of them used pre-selected stimulus material in an experimental setting. Yet, several ways to measure emotional response were applied, and it is therefore challenging to compare the results. Only two studies considered further content types and their effect on emotional responses. The contradictory findings of content with transactional appeal could be attributed to the different social media platforms that were used as medium. While Yu [64] analyzed content effects on Facebook, Kuan et al. [30] focused on content from Groupon, a group buying platform, which has a specific focus on transactional content.
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