The effects of visual formats on Facebook health-related posts: evidence from eye movement analysis

PurposeThis study aimed to provide user-centered evidence for health professionals to make optimal use of images for the effective dissemination of health information on Facebook (FB).Design/methodology/approachUsing an eye-tracking experiment and a survey method, this study examined 42 participants' reading patterns as well as recall and recognition outcomes with 36 FB health information posts having various FB post features.FindingsThe findings demonstrated that FB posts with text-embedded images received more attention and resulted in the highest recall and recognition. Meanwhile, compared to text-embedded images, visual only images yielded less effective recall of information, but they caught the viewers' attention; graphics tended to attract more attention than photos. For effective communication, the text features in FB posts should align with the formats of the images.Practical implicationsThe findings of this study provide practical implications for health information disseminators by suggesting that text-embedded images should be used for effective health communication.Originality/valueThis study provided evidence of users' different viewing patterns for FB health information posts and the relationship between FB post types and recall and recognition outcomes.

Germania de Tácito: en los límites del género literario

This article proposes a new generic label for Tacitus’ Germania as “frontier ethnography”. Our reading is supported by Germania’s textual instability, due to its topical originality and compositive innovation. Although these features place Germania in a disruptive positioning face of historiographical tradition of Monography, it is consistent with the particular rhetorical situation of the late first century AD, traversed by the mixture of genres and the inversion of center-periphery relationships, and with the rise of a new dynasty as well. These characteristics are found in the two main text features of Germania. On the one hand, Ethnography, which was traditionally relegated to the excursus, is used here as the text’s main narrative device, whereas historical discourse is relocated to the digression. On the other hand, Barbaric periphery beyond the frontier becomes the central narrative matter of the text. Therefore, these textual features allow us to state that Germania insinuates a discourse move towards the limits of Roman generic and geographical space. Hence, Tacitus’ Germania can be interpreted as a literary exercise representing a new space within its sociopolitical context: the frontier.

A Cross-Modal Image and Text Retrieval Method Based on Efficient Feature Extraction and Interactive Learning CAE

In view of the complexity of the multimodal environment and the existing shallow network structure that cannot achieve high-precision image and text retrieval, a cross-modal image and text retrieval method combining efficient feature extraction and interactive learning convolutional autoencoder (CAE) is proposed. First, the residual network convolution kernel is improved by incorporating two-dimensional principal component analysis (2DPCA) to extract image features and extracting text features through long short-term memory (LSTM) and word vectors to efficiently extract graphic features. Then, based on interactive learning CAE, cross-modal retrieval of images and text is realized. Among them, the image and text features are respectively input to the two input terminals of the dual-modal CAE, and the image-text relationship model is obtained through the interactive learning of the middle layer to realize the image-text retrieval. Finally, based on Flickr30K, MSCOCO, and Pascal VOC 2007 datasets, the proposed method is experimentally demonstrated. The results show that the proposed method can complete accurate image retrieval and text retrieval. Moreover, the mean average precision (MAP) has reached more than 0.3, the area of precision-recall rate (PR) curves are better than other comparison methods, and they are applicable.

An Improved Method of Automated Nonparametric Content Analysis for Social Science

Some scholars build models to classify documents into chosen categories. Others, especially social scientists who tend to focus on population characteristics, instead usually estimate the proportion of documents in each category—using either parametric “classify-and-count” methods or “direct” nonparametric estimation of proportions without individual classification. Unfortunately, classify-and-count methods can be highly model-dependent or generate more bias in the proportions even as the percent of documents correctly classified increases. Direct estimation avoids these problems, but can suffer when the meaning of language changes between training and test sets or is too similar across categories. We develop an improved direct estimation approach without these issues by including and optimizing continuous text features, along with a form of matching adapted from the causal inference literature. Our approach substantially improves performance in a diverse collection of 73 datasets. We also offer easy-to-use software that implements all ideas discussed herein.

AttendAffectNet–Emotion Prediction of Movie Viewers Using Multimodal Fusion with Self-Attention

In this paper, we tackle the problem of predicting the affective responses of movie viewers, based on the content of the movies. Current studies on this topic focus on video representation learning and fusion techniques to combine the extracted features for predicting affect. Yet, these typically, while ignoring the correlation between multiple modality inputs, ignore the correlation between temporal inputs (i.e., sequential features). To explore these correlations, a neural network architecture—namely AttendAffectNet (AAN)—uses the self-attention mechanism for predicting the emotions of movie viewers from different input modalities. Particularly, visual, audio, and text features are considered for predicting emotions (and expressed in terms of valence and arousal). We analyze three variants of our proposed AAN: Feature AAN, Temporal AAN, and Mixed AAN. The Feature AAN applies the self-attention mechanism in an innovative way on the features extracted from the different modalities (including video, audio, and movie subtitles) of a whole movie to, thereby, capture the relationships between them. The Temporal AAN takes the time domain of the movies and the sequential dependency of affective responses into account. In the Temporal AAN, self-attention is applied on the concatenated (multimodal) feature vectors representing different subsequent movie segments. In the Mixed AAN, we combine the strong points of the Feature AAN and the Temporal AAN, by applying self-attention first on vectors of features obtained from different modalities in each movie segment and then on the feature representations of all subsequent (temporal) movie segments. We extensively trained and validated our proposed AAN on both the MediaEval 2016 dataset for the Emotional Impact of Movies Task and the extended COGNIMUSE dataset. Our experiments demonstrate that audio features play a more influential role than those extracted from video and movie subtitles when predicting the emotions of movie viewers on these datasets. The models that use all visual, audio, and text features simultaneously as their inputs performed better than those using features extracted from each modality separately. In addition, the Feature AAN outperformed other AAN variants on the above-mentioned datasets, highlighting the importance of taking different features as context to one another when fusing them. The Feature AAN also performed better than the baseline models when predicting the valence dimension.

A Novel Classification Model SA-MPCNN for Power Equipment Defect Text

The text classification of power equipment defect is of great significance to equipment health condition evaluation and power equipment maintenance decisions. Most of the existing classification methods do not sufficiently consider the semantic relation between words in the same sentence and cannot extract deep semantic features. To tackle those problems, this article proposes a novel classification method by combining the self-attention mechanism and multi-channel pyramid convolution neural networks. We utilize the bidirectional gated recurrent unit to model the text sequence and, on this basis, improve self-attention layer to dot multiplication on the forward and backward features to obtain the global attention score. Thereby, effective features are enhanced, invalid features are weakened, and important text representation vectors are obtained. To solve the problem that the shallow network structure cannot extract deep semantic features, we design a multi-channel pyramid convolution network, which first extracts deep text features from the channels of different windows and then fuses the text features of each channel. By comparing with the state-of-the-art methods, the model in this article has better performance in text classification of power equipment defects.

Text Mining for Cyberbullying Detection: a Brazilian Portuguese Evaluation

Bullying and cyberbullying are words commonly seen in today's news. Although the scientific community has evaluated text mining techniques for cyberbullying detection, few studies have targeted Brazilian Portuguese datasets. Our study aims to assess the text mining application to detect cyberbullying messages written in Brazilian Portuguese. We gathered posts and comments from Reddit communities and extracted several text features. We then processed these features using Naïve Bayes and SVM classifiers to uncover cyberbullying activity. The outcomes of this experiment may not be used solo for cyberbullying detection; however, they can aid moderators in prioritizing content reviews and acting faster on real cyberbullying cases.

A Herd Effect Detection Method Based on Text Features

The herd effect is a common phenomenon in social society. The detection of this phenomenon is of great significance in many tasks based on social network analysis such as recommendation. However, the research on social network and natural language processing seldom focuses on this issue. In this paper, we propose an unsupervised data mining method to detect herding in social networks. Taking shopping review as an example, our algorithm can identify other reviews which are affected by some previous reviews and detect a herd effect chain. From the overall perspective, the cross effects of all views form the herd effect graph. This algorithm can be widely used in various social network analysis methods through graph structure, which provides new useful features for many tasks.

A tree based approach for multi-class classification of surgical procedures using structured and unstructured data

Background In surgical department, CPT code assignment has been a complicated manual human effort, that entails significant related knowledge and experience. While there are several studies using CPTs to make predictions in surgical services, literature on predicting CPTs in surgical and other services using text features is very sparse. This study improves the prediction of CPTs by the means of informative features and a novel re-prioritization algorithm. Methods The input data used in this study is composed of both structured and unstructured data. The ground truth labels (CPTs) are obtained from medical coding databases using relative value units which indicates the major operational procedures in each surgery case. In the modeling process, we first utilize Random Forest multi-class classification model to predict the CPT codes. Second, we extract the key information such as label probabilities, feature importance measures, and medical term frequency. Then, the indicated factors are used in a novel algorithm to rearrange the alternative CPT codes in the list of potential candidates based on the calculated weights. Results To evaluate the performance of both phases, prediction and complementary improvement, we report the accuracy scores of multi-class CPT prediction tasks for datasets of 5 key surgery case specialities. The Random Forest model performs the classification task with 74–76% when predicting the primary CPT (accuracy@1) versus the CPT set (accuracy@2) with respect to two filtering conditions on CPT codes. The complementary algorithm improves the results from initial step by 8% on average. Furthermore, the incorporated text features enhanced the quality of the output by 20–35%. The model outperforms the state-of-the-art neural network model with respect to accuracy, precision and recall. Conclusions We have established a robust framework based on a decision tree predictive model. We predict the surgical codes more accurately and robust compared to the state-of-the-art deep neural structures which can help immensely in both surgery billing and scheduling purposes in such units.

Intention based Clustering of Relevant Reviews using Content Similarity

The proposed work deals with finding related reviews posted on various online Forums. Conventional methods for matching related documents compute the content similarity over the entire review instead of partitioning into segments revealing different intentions. In this work, intention-based similarity clustering is introduced to find the relatedness of two documents. This method forms the document clusters based on the similarity of the segments with similar intentions. The segmentation points are identified using a number of text features which can express when the segmentation should be done. Finally, the document clusters are formed by grouping the segments with similar intentions in same cluster and then the similarities among the segments with the same intention are computed. The proposed model is trained on TripAdvisor and Yelp Open Review datasets to evaluate the performance of the model, and the evaluation results show that the model produces more precise results in mining documents related to the user’s interest.