Posting,commenting, and tagging: Effects of sharing news stories on Facebook

Social networking sites such as Facebook provide new ways of sharing news stories that allow users to act as opinion leaders in their networks, encourage discussion, and potentially increase their involvement in current events. This study identifies the particular features of Facebook that facilitate the discussion of news and tests their effects on involvement and feelings of influence. Participants (N = 265) in a 3 (Broadcast level: news feed vs. wall post vs. direct message) × 3 (Elaboration: opinion vs. question vs. no comment) × 2 (Involving-friends: tag vs. no tag) between-subjects factorial experiment were randomly assigned to share a story from a news website on Facebook. Results show that user involvement in the news content depends on the social affordances of the site, particularly those that allow for audience customization and those that drive network feedback. Asking the network’s opinions and targeting specific friends led to greater involvement in the news content. Discussion through comments led to a greater sense of influence and greater involvement for those sharing the news story. These findings highlight the importance of encouraging individuals to act as sources of information in their networks to drive engagement in current events in the changing news landscape.     

The effects of source cues on online news perception

Among various interface cues, expertise, identity, and bandwagon cues have been consistently found to have significant effects on media users’ perceptions of online news content. To examine the effects of these three types of heuristic cues in the context of online news consumption, the current study involved a 2 (expertise cue: low vs. high) × 2 (identity cue: in-group vs. out-group) × 2 (bandwagon cue: low vs. high) online experiment. A total of 121 undergraduate students participated in the study. Significant two-way interaction effects between the expertise and bandwagon cues on perceived credibility suggested the positive combined effect of these two cues. Moreover, significant three-way interaction effects among expertise, identity and bandwagon cues indicated that the interaction effects between expertise and bandwagon cues tend to work as a function of the identity cue. While confirming the importance of the identity cue in users’ perceptions of online news, three-way interaction effects confirmed the co-occurrence of heuristic and systematic processing. The interaction effects also suggested that people process news systematically when the recommenders are out-group members, whereas they process news heuristically when the recommenders are in-group members. Theoretical as well as practical implications have also been discussed in this article.     

Triggering participation: Exploring the effects of third-person and hostile media perceptions on online participation

Using a 2 × 3 mixed between-within subjects experiment (N = 102), we tested how the presence of online comments affects self-other differences and perceptions of media bias, as well as factors predicting subjects’ likelihood of commenting on an online news story. We found that (a) presence of comments lowers self-other differences and consequently attenuates the third-person effect, and (b) perceptions of media bias significantly predict likelihood of commenting. Additionally, we found that subjects were more likely to comment on stories they found biased against their position as a form of corrective action, and that subjects were more likely to share and like stories they found biased in favor of their position as a form of promotional action.     

Virtual vs. real body in exergames: Reducing social physique anxiety in exercise experiences

Research indicates that people with body image dissatisfaction (BID) are not benefited from exercising in group contexts. The current study tested whether exercise video games (exergames) can provide unique opportunities for exercise interventions through the use of avatars. An experiment was conducted using a 2 (BID: High vs. Low) × 2 (Exercise context: Solitary vs. Group) between-subjects design. Results demonstrated that individuals with high BID reported similar or more favorable exergame experiences compared to individuals with low BID. Further, individuals’ social physique anxiety was significantly reduced during exergame play. Self-presence mediated the relationship between exercise context and exergame experiences.     

Promote physical activity among college students: Using media richness and interactivity in web design

Recently with widely available access, the web has emerged as a medium for new interventions. However, as yet, little is known about what makes some websites more effective than others. This study investigated an approach to developing websites that utilized two media characteristics – media richness and interactivity – to promote physical activity among college students. Four forms of websites were developed and tested in a 2 × 2 between-subject experiment (high vs. low richness; high vs. low interactivity) that was conducted among 205 participants. Overall, media richness had a significant main effect on college students’ intention to visit the fitness center while interactivity influenced the likelihood they would recommend it. Although media richness did not have a significant main effect on recommendation, a significant interaction effect was observed that rich media led to higher recommendation intention when interactivity level was low. In addition, knowledge, attitude and trustworthiness of the fitness center mediated the effects of media richness and interactivity on behavioral intentions. These findings support the efficacy of utilizing these media characteristics to design web-based health interventions promoting college students’ physical activity.     

Effects of visual and linguistic anthropomorphic cues on social perception,self-awareness,and information disclosure in a health website

Acknowledging the lack of studies examining both visual and linguistic anthropomorphic cues and the underlying mechanisms of their effects, we investigated how the different modalities of anthropomorphic cues in a health website influenced information disclosure. In a 2 (visual cues: human vs. non-human image) × 2 (linguistic cues: conversational vs. impersonal language) × 2 (question type: less vs. more sensitive questions) between-subjects experiment (N = 254), participants registered with a mock-up health website. We assessed a behavioral outcome of not disclosing personal information and psychological outcomes of social perception and self-awareness as potential mediators. Results revealed distinctive effects of the two modalities of the anthropomorphic cues. Anthropomorphic images, on one hand, increased public and private self-awareness, and public self-awareness in turn led to less information disclosure. Anthropomorphic language, on the other hand, heightened social perception and promoted information disclosure, but social perception did not predict the disclosure. These results indicate unique underlying mechanisms of the effects of anthropomorphism: priming effect of visual cues, and communicative effects of linguistic cues.     

Damaged corporate reputation: Can celebrity Tweets repair it?

These days, many corporations engage in Twitter activities as a part of their communication strategy. Corporations can use this medium to share information with stakeholders, to answer customer questions, or to build on their image. In this study we examined the extent to which celebrity Tweet messages can be used to repair a damaged corporate reputation, and how this message should be designed and what celebrity should be ‘used’.In two experiments, a 2 × 2 (attractive celebrity versus intelligent celebrity) × (personal message versus general message) design was used. In total, 163 respondents first expressed their feelings regarding the two organisations in a baseline reputation measurement (M = 4.72 on 7 point Likert scale). After that a news items was presented communicating a big fraud and mismanagement, resulting in a decreased reputation score (M = 4.10). In the final stage one of the four experimental Tweets was presented, aimed at repairing the damaged reputation, which succeeded (M = 4.43). For both organisations, the crisis prime significantly decreased reputation scores, and the Tweet significantly increased reputation score again. The analysis of variance shows a main effect for type of celebrity. In our experiment the intelligent celebrity’s Tweet was best to use.The study reveals that celebrities’ Tweets can restore a positive public opinion about corporations. This study shows that when it comes to serious matters, an intelligent celebrity, who has the best fit with the topic, is of best impact. Consequences for corporate communication and future research are discussed.     

Social media metrics: Third-person perceptions of health information

The present study investigated the role of social media metrics (i.e., number of shares and comments) displayed alongside online news stories in shaping users’ perceptions of the content and its influence. In a web-based experiment (N = 144), participants first read a cancer news story that displayed either a high or a low level of social media metrics, then reported their perceived story influence on the self and others, as well as their behavioral intentions. In the low social media metrics condition, the general story influence was perceived to be stronger for others than for the self, indicative of the “third-person effect.” This effect, however, was diminished to insignificant levels in the high social media metrics condition. Further, social media metrics had an ultimate indirect effect on behavioral intentions via the third-person effect. Theoretical and practical implications of these findings are provided in the end.     

Continuous blood pressure estimation based on multiple parameters from eletrocardiogram and photoplethysmogram by Back-propagation neural network

The cuff-less continuous blood pressure monitoring provides reliable and invaluable information about the individuals’ health condition. Conventional sphygmomanometer with a cuff measures only the value of the blood pressure intermittently and the measurement process is sometimes inconvenient. In this work, a systematic approach with multi-parameter fusion has been proposed to estimate the non-invasive beat-to-beat systolic and diastolic blood pressure with high accuracy. The methods involve real-time monitoring of the electrocardiogram (ECG) and photoplethysmogram (PPG), and extracting the R peak from the ECG and relevant feature parameters from the synchronous PPG. Also, it covers the creation of the topological model of back-propagation neural network that has fifteen neurons in the input layer, ten neurons in the single interlayer, and two neurons in the output layer, where all the neurons are fully connected. As for the results, the proposed method was validated on the volunteers. The reference blood pressure (BP) is from Finometer (MIDI, Finapres Medical System, Netherlands). The results showed that the mean ± S.D. for the estimated systolic BP (SBP) and diastolic BP (DBP) with the proposed method against reference were −0.41 ± 2.02 mmHg and 0.46 ± 2.21 mmHg, respectively. Thus, the continuous blood pressure algorithm based on Back-Propagation neural network provides a continuous BP with a high accuracy.     

Recognizing people’s identity in construction sites with computer vision: A spatial and temporal attention pooling network

Several prototype vision-based approaches have been developed to capture and recognize unsafe behavior in construction automatically. Vision-based approaches have been difficult to use due to their inability to identify individuals who commit unsafe acts when captured using digital images/video. To address this problem, we applied a novel deep learning approach that utilizes a Spatial and Temporal Attention Pooling Network to remove redundant information contained in a video to enable a person’s identity to be automatically determined. The deep learning approach we have adopted focuses on: (1) extracting spatial feature maps using the spatial attention network; (2) extracting temporal information using the temporal attention networks; and (3) recognizing a person’s identity by computing the distance between features. To validate the feasibility and effectiveness of the adopted deep learning approach, we created a database of videos that contained people performing their work on construction sites, conducted an experiment, and then performed k-fold cross-validation. The results demonstrated that the approach could accurately identify a person’s identity from videos captured from construction sites. We suggest that our computer-vision approach can potentially be used by site managers to automatically recognize those individuals that engage in unsafe behavior and therefore be used to provide instantaneous feedback about their actions and possible consequences.     

Students’ perceptions of creativity in learning Information Technology (IT) in project groups

This paper explores students’ perceptions of creativity in learning Information Technology (IT) in project groups and the implications of better educating creative IT students for the future. Theoretically, the extension of social psychology research into creativity lays the basis of bringing creativity, learning and IT education into one framework. Empirically, qualitative interviews were carried out with 48 students from three disciplines, including Computer Science (n = 16), Electronic Systems (n = 15) and Medialogy (n = 17) at Aalborg University (AAU) in Denmark, which has a tradition of using problem-based learning (PBL) in student project groups. According to the findings, the students’ perceptions of creativity reflect their domain-related conceptualization and tacit learning experience, with different levels of confidence of being creative persons. As IT plays multiple roles in developing students’ creativity, it can be regarded as a ‘learning partner’. This implies that in the future creativity should be taught more explicitly, helping students to become creative IT talents as a part of their professional identity. It also requires teaching efforts to build a learning environment that stimulates creativity more effectively through more interactions between learners, learning tasks and learning tools.     

Effects of captions and time-compressed video on learner performance and satisfaction

Digital video is becoming increasingly popular in higher education with faculty digitally recording and broadcasting lectures for students to learn-on-demand, such as iTunes University or YouTube. Students have discovered accelerated playback features in popular computer software and use it to reduce the amount of time watching video-enhanced instruction. In the current study, 147 undergraduate students were randomly assigned to one of six video treatments based on a 3 Video Speed (1.0 = Normal vs. 1.25 = Fast vs. 1.50 = Very Fast) × 2 Captions (Captions Present vs. Captions Absent) × 2 Trial (Trial 1 vs. Trial 2) design. Results show no significant difference on learner performance across treatments based on Video Speed. Captions were found to have a significant negative effect on learner performance. A significant difference was found on learner satisfaction in favor of a normal Video Speed. The findings suggest that learners might be able to accelerate Video Speeds up to 1.5 times the normal speed, but are generally less satisfied with the learning experience.     

The role of sunk costs in online consumer decision-making

Sunk costs, which can cause inconsistencies between consumer behavior and economic theory, have been widely studied in different research areas and among various consumer groups. Nevertheless, the authors found that to date there has been no empirical research examining how sunk costs affect consumer behavior with regard to online shopping in different product types. Therefore, this study used the following 2 × 2 × 2 experimental design to study the effects of sunk costs on consumers’ online shopping decisions. The results show that (1) sunk costs have significant effects on consumer online shopping behavior. However, due to the different product characteristics, the impacts were weaker effective when consumers bought search goods than when they purchased experience goods; (2) the sunk costs (Membership fee vs. Deposit) of an initial choice and a new choice had interaction effects on consumer intention to purchase a new product; and (3) the sunk costs and service quality of an initial choice and new choice would affect the level of regret felt with regard to the consumer’s initial choice. In addition, some recommendations on market positioning and service quality design are made, which practitioners can refer to when formulating marketing strategies.     

Just-in-time,schematic supportive information presentation during cognitive skill acquisition

Cognitive load theory states that well-designed learning material minimizes extraneous cognitive load and optimizes germane cognitive load within the thresholds of available cognitive resources. In this study, the extraneous cognitive load is minimized by avoiding temporal split attention with regard to supportive information (i.e., conceptual models or ‘theory’) and the germane cognitive load is optimized by using schematic representations of this information to direct learner’s attention to concepts relevant for learning. A 2 × 2 between-groups design with the factors supportive information (before or during practice) and schematic representation (before or during practice) was used to investigate whether this balance between extraneous and germane load leads to more effective and efficient learning. It was found that the ‘supportive during, schema before’ format indeed yielded a higher learning efficiency than the ‘supportive before, schema before’ and the supportive during, schema during’ format but no differences were found for learning effectiveness (i.e., test performance).     

Selective reading of large online forum discussions: The impact of rating visualizations on navigation and learning

As discussions in online forums can become quite large, participants must be highly selective in their reading behavior. Standard, chronological displays of discussions provide little guidance in how to find valuable content. An experimental study tested whether a group awareness tool can support individual navigation and subsequent learning within a forum. 127 participants read through a large online discussion in which contributions were rated on average quality and average agreement. In a 2 × 2 factorial design, posts were visualized along continua indicating average quality ratings (absent vs. present) and/or average agreement ratings (absent vs. present). Results showed that the visualization of discussion contributions led to stronger deviations from a chronological reading order. Moreover, it was found that the presence of a quality dimension shifted readers’ focus to high-quality contributions (unipolar navigation). Moreover, the quality dimension was weakly associated with better performance in a knowledge test. In contrast, it was found that the presence of an agreement dimension shifted readers’ focus to both high-agreement and low-agreement contributions (bipolar navigation). Moreover, the agreement dimension was associated with stronger attitude change and a higher intention to participate. Implications for the use of group awareness tools in full-scale online forum environments are drawn.     

3D-SDAR modeling of hERG potassium channel affinity: A case study in model design and toxicophore identification

A dataset of 237 human Ether-à-go-go Related Gene (hERG) potassium channel inhibitors (180 of which were used for model building and validation, whereas 57 constituted the “true” external prediction set) collected from 22 literature sources was modeled by 3D-SDAR. To produce reliable and reproducible classification models for hERG blocking, the initial set of 180 chemicals was split into two subsets: a balanced modeling set consisting of 118 compounds and an unbalanced validation set comprised of 62 compounds. A PLS bagging-like algorithm written in Matlab was used to process the data and assign each compound to one of the two (hERG+ or hERG-) activity classes. The best predictive model evaluated on the basis of a fully randomized hold-out test set (comprising 20% of the modeling set) used 4 latent variables and a grid of 6 ppm × 6 ppm × 1 Å in the C-C region, 6 ppm × 30 ppm × 1 Å in the C-N region, and 30 ppm × 30 ppm × 1 Å in the N-N region. An overall accuracy of 0.84 was obtained for both the hold-out test set and the validation set. Further, an external prediction set consisting of 57 drugs and drug derivatives was used to estimate the true predictive power of the reported 3D-SDAR model – a slight reduction of the overall accuracy down to 0.77 was observed. 3D-SDAR map of the most frequently occurring bins and their projection on the standard coordinate space of the chemical structures allowed identification of a three-center toxicophore composed of two aromatic rings and an amino group. A U test along the distance axis of the most frequently occurring 3D-SDAR bins was used to set the distance limits of the toxicophore. This toxicophore was found to be similar to an earlier reported phospholipidosis (PLD) toxicophore.     

“It’s Not Who You Know,but Who You Add:” An investigation into the differential impact of friend adding and self-disclosure on interpersonal perceptions on Facebook

This research seeks to understand how people perceive and respond to structural factors and different types of disclosure on Facebook when evaluating the profile of someone they have never met offline. Using a 2 × 3 × 2 between-subjects experimental design, this research explored the relationship between friend adding (add; no add), levels of self-disclosure (low, medium, high), and sex of the Facebook profile owner (male; female) on feelings of interpersonal liking, future behavioral intentions to interact, and homophily. Results indicated that friend adding and higher levels of self-disclosure led to greater feelings of interpersonal liking and homophily amongst both male and female participants. In addition, males tended to view other male profiles with moderate amounts of disclosure and female profiles with the highest amount of disclosure most favorably. Theoretical and practical implications are discussed.     

Modeling and simulation of the open-end equity mutual fund market in Taiwan by using self-organizing map

This research applies artificial intelligence (AI) of unsupervised learning self-organizing map neural network (SOM-NN) to establish a model to select the superior funds. This research period is from year 2000 to 2010 and picks 100 domestic equity mutual funds as study object. This research used 30 days prior to the beginning of each month’s prior 30 days, 60 days, 90 days on fund’s net asset value and the Taiwan Weighted Stock Index (TAIEX) return as the fund’s relative performance evaluation indicators classified by month. Finally, based on the superior rate or the average return rate, this research select the superior funds and simulate investment transactions according to this model.The empirical results show that using the mutual fund’s net asset value and the TAIEX’s relative return as SOM-NN input variables not only finds out the superior fund but also has a good predictive ability. Applying this model to simulate investment transactions will be better than the random trading model and market. The experiments also found that the investment simulation of a three-month interval has the highest profitability. The model operation suggests that it is more suitable for short-term and medium-term investment. This research can assist investors in making the right investment decisions while facing rapid financial environment changes.     

Human activity recognition with smartphone sensors using deep learning neural networks

Human activities are inherently translation invariant and hierarchical. Human activity recognition (HAR), a field that has garnered a lot of attention in recent years due to its high demand in various application domains, makes use of time-series sensor data to infer activities. In this paper, a deep convolutional neural network (convnet) is proposed to perform efficient and effective HAR using smartphone sensors by exploiting the inherent characteristics of activities and 1D time-series signals, at the same time providing a way to automatically and data-adaptively extract robust features from raw data. Experiments show that convnets indeed derive relevant and more complex features with every additional layer, although difference of feature complexity level decreases with every additional layer. A wider time span of temporal local correlation can be exploited (1 × 9–1 × 14) and a low pooling size (1 × 2–1 × 3) is shown to be beneficial. Convnets also achieved an almost perfect classification on moving activities, especially very similar ones which were previously perceived to be very difficult to classify. Lastly, convnets outperform other state-of-the-art data mining techniques in HAR for the benchmark dataset collected from 30 volunteer subjects, achieving an overall performance of 94.79% on the test set with raw sensor data, and 95.75% with additional information of temporal fast Fourier transform of the HAR data set.     

A 24-h forecast of ozone peaks and exceedance levels using neural classifiers and weather predictions

A neural network combined to a neural classifier is used in a real time forecasting of hourly maximum ozone in the centre of France, in an urban atmosphere. This neural model is based on the MultiLayer Perceptron (MLP) structure. The inputs of the statistical network are model output statistics of the weather predictions from the French National Weather Service. These predicted meteorological parameters are very easily available through an air quality network. The lead time used in this forecasting is (t + 24) h. Efforts are related to a regularisation method which is based on a Bayesian Information Criterion-like and to the determination of a confidence interval of forecasting. We offer a statistical validation between various statistical models and a deterministic chemistry-transport model. In this experiment, with the final neural network, the ozone peaks are fairly well predicted (in terms of global fit), with an Agreement Index = 92%, the Mean Absolute Error = the Root Mean Square Error = 15 μg m⁻³ and the Mean Bias Error = 5 μg m⁻³, where the European threshold of the hourly ozone is 180 μg m⁻³.To improve the performance of this exceedance forecasting, instead of the previous model, we use a neural classifier with a sigmoid function in the output layer. The output of the network ranges from [0,1] and can be interpreted as the probability of exceedance of the threshold. This model is compared to a classical logistic regression. With this neural classifier, the Success Index of forecasting is 78% whereas it is from 65% to 72% with the classical MLPs. During the validation phase, in the Summer of 2003, six ozone peaks above the threshold were detected. They actually were seven.Finally, the model called NEUROZONE is now used in real time. New data will be introduced in the training data each year, at the end of September. The network will be re-trained and new regression parameters estimated. So, one of the main difficulties in the training phase – namely the low frequency of ozone peaks above the threshold in this region – will be solved.