Perpetuating online sexism offline: Anonymity,interactivity, and the effects of sexist hashtags on social media

Sexism and sexual harassment are not uncommon in online environments such as social networking sites, forums, and video games. This experiment investigated whether users’ anonymity and level of interactivity with sexist content on social media influenced sexist attitudes and offline behavior. Participants (N = 172) used a Twitter account that was anonymous or had personally identifying details. They were asked to share (i.e., retweet) or write posts incorporating a sexist hashtag. After exposure, participants completed two purportedly unrelated tasks, a survey and a job hiring simulation in which they evaluated male and female candidates’ resumés. Anonymous participants reported greater hostile sexism after tweeting than nonanonymous participants. Participants who composed sexist tweets reported greater hostile sexism and ranked female job candidates as less competent than those who retweeted, although this did not significantly affect their likelihood to hire.     

Knowledge sharing and social media: Altruism,perceived online attachment motivation,and perceived online relationship commitment

Social media, such as Facebook and Twitter, have become extremely popular. Facebook, for example, has more than a billion registered users and thousands of millions of units of information are shared every day, including short phrases, articles, photos, and audio and video clips. However, only a tiny proportion of these sharing units trigger any type of knowledge exchange that is ultimately beneficial to the users. This study draws on the theory of belonging and the intrinsic motivation of altruism to explore the factors contributing to knowledge sharing behavior. Using a survey of 299 high school students applying for university after the release of the public examination results, we find that perceived online attachment motivation (β = 0.31, p < 0.001) and perceived online relationship commitment (β = 0.49, p < 0.001) have positive, direct, and significant effects on online knowledge sharing (R² 0.568). Moreover, when introduced into the model, altruism has a direct and significant effect on online knowledge sharing (β = 0.46, p < 0.001) and the total variance explained by the extended model increases to 64.9%. The implications of the findings are discussed.     

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.     

Exploring depression and problematic internet use among college females: A multisite study

ObjectiveThe purpose of this study was to assess associations between depression and problematic internet use (PIU) among female college students, and determine whether Internet use time moderates this relationship.MethodThis cross-sectional survey included 265 female college students from four U.S. universities. Students completed the Patient Health Questionnaire-9 (PHQ-9), the Problematic and Risky Internet Use Screening Scale (PRIUSS) and self-reported daily Internet use. Analyses included multivariate analysis of variance and Poisson regression.ResultsParticipants reported mean age of 20.2 years (SD = 1.7) and were 84.9% Caucasian. The mean PHQ-9 score was 5.4 (SD = 4.6); the mean PRIUSS score was 16.4 (SD = 11.1). Participants’ risk for PIU increased by 27% with each additional 30 min spent online using a computer (RR = 1.27, 95% CI: 1.14–1.42, p < .0001). Risk for PIU was significantly increased among those who met criteria for severe depression (RR = 8.16 95% CI: 4.27–15.6, p < .0001). The PHQ-9 items describing trouble concentrating, psychomotor dysregulation and suicidal ideation were most strongly associated with PIU risk.ConclusionsThe positive relationship between depression and PIU among female college students supports screening for both conditions, particularly among students reporting particular depression symptoms.     

A ranking procedure based on a natural monotonicity constraint

We formulate a new ranking procedure in the traditional context where each voter has expressed a linear order relation or ranking over the candidates. The final ranking of the candidates is taken to be the one which best adheres to a natural monotonicity constraint. For a ranking a ≻ b ≻ c, monotonicity implies that the strength with which a ≻ c is supported should not be less than the strength with which either one of a ≻ b or b ≻ c is supported. We investigate some properties of this ranking procedure and encounter some surprising preliminary results.     

Time,place, technology: Twitter as an information source in the Wisconsin labor protests

Recently, Twitter has become a prominent part of social protest movement communication. This study examines Twitter as a new kind of citizen journalism platform emerging at the aggregate in the context of such “crisis” situations by undertaking a case study of the use of Twitter in the 2011 Wisconsin labor protests. A corpus of more than 775,000 tweets tagged with #wiunion during the first 3 weeks of the protests provides the source of the analyses. Findings suggest that significant differences exist between users who tweet via mobile devices, and thus may be present at protests, and those who tweet from computers. Mobile users post fewer URLs overall; however, when they do, they are more likely to link to traditional news sources and to provide additional hashtags for context. Over time, all link-posting declines, as users become better able to convey first-hand information. Notably, results for most analyses significantly change when restricted to original tweets only, rather than including retweets.     

3D-QSAR analysis of TRPV1 inhibitors reveals a pharmacophore applicable to diverse scaffolds and clinical candidates

TRPV1 (Transient Receptor Potential Vanilloid Type 1) receptor, a member of Transient Receptor Potential Vanilloid subfamily of ion channels, occurs in the peripheral and central nervous system, and plays a key role in transmission of pain. Consequently, this has been the target for discovery of several pain relieving agents which have undergone clinical trials. Though several TRPV1 antagonists have progressed to become clinical candidates, many are known to cause temperature elevation in humans, halting their further advancement, and signifying the need for new chemotypes. Different chemical classes of TRPV1 antagonists share three important features: an amide or an isostere flanked by an aromatic (or fused aromatic) ring with polar substitutions on one side, and a hydrophobic group on the other. Recent work identified new series of compounds with these and additional features, leading to improvement of properties, and development of clinical candidates. Herein, we describe a 3D-QSAR model (n = 62; R² = 0.9 and Q² = 0.75) developed from the piperazinyl-aryl series of compounds and a novel 5-point pharmacophore model is shown to fit several diverse scaffolds, six clinical candidates, five pre-clinical candidates and three lead compounds. The pharmacophore model can aid in finding new chemotypes as starting points that can be developed further.     

The interplay between immersion and appeal in video games

Immersion and appeal are considered to be necessary constituents of the player experience. In this article their relationship is examined through a 2 × 2 factorial study (n = 173) in the context of two games, a first-person shooter and a massively multi-player online role-playing game, and in the context of two types of players: experienced players who have never played the game in one of the genres in question, and experienced players who have played one of the games in question. It is found that immersion and appeal are linearly correlated, and the repercussions of this finding are discussed.     

Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3

ESA's upcoming satellites Sentinel-2 (S2) and Sentinel-3 (S3) aim to ensure continuity for Landsat 5/7, SPOT-5, SPOT-Vegetation and Envisat MERIS observations by providing superspectral images of high spatial and temporal resolution. S2 and S3 will deliver near real-time operational products with a high accuracy for land monitoring. This unprecedented data availability leads to an urgent need for developing robust and accurate retrieval methods. Machine learning regression algorithms may be powerful candidates for the estimation of biophysical parameters from satellite reflectance measurements because of their ability to perform adaptive, nonlinear data fitting.By using data from the ESA-led field campaign SPARC (Barrax, Spain) we have compared the utility of four state-of-the-art machine learning regression algorithms and four different S2 and S3 band settings to assess three important biophysical parameters: leaf chlorophyll content (Chl), leaf area index (LAI) and fractional vegetation cover (FVC). The tested Sentinel configurations were: S2-10 m (4 bands), S2-20 m (8 bands), S2-60 m (10 bands) and S3-300 m (19 bands), and the tested methods were: neural networks (NN), support vector regression (SVR), kernel ridge regression (KRR), and Gaussian processes regression (GPR).GPR outperformed the other retrieval methods for the majority of tested configurations and was the only method that reached the 10% precision required by end users in the estimation of Chl. Also, although validated with an RMSE accuracy around 20%, GPR yielded optimal LAI and FVC estimates at highest S2 spatial resolution of 10 m with only four bands. In addition to high accuracy values, GPR also provided confidence intervals of the estimates and insight in relevant bands, which are key advantages over the other methods. Given all this, GPR proved to be a fast and accurate nonlinear retrieval algorithm that can be potentially implemented for operational monitoring applications.     

Who puts the best “face” forward on Facebook?: Positive self-presentation in online social networking and the role of self-consciousness,actual-to-total Friends ratio,and culture

The present research investigated how individual, interpersonal, and cultural variables influence positive self-presentation in online social networking. In particular, we examined the role of self-consciousness, actual-to-total Friends ratio, and culture in positive self-presentation on Facebook. A cross-sectional survey was conducted with college-age participants in the United States (n = 183) and South Korea (n = 137). Results showed that self-consciousness (public vs. private) and actual-to-total Friends ratio were not significantly associated with positive self-presentation on Facebook; however, culture showed a statistically significant association with positive self-presentation on Facebook, with the U.S. participants engaging in positive self-presentation on Facebook to a greater extent than the South Korean participants. More interestingly, culture significantly moderated the relationship between public self-consciousness and positive self-presentation as well as the relationship between actual-to-total Friends ratio and positive self-presentation. Specifically, positive self-presentation showed a significant positive association with public self-consciousness and a significant negative association with actual-to-total Friends ratio only among the South Korean participants and not among the U.S. participants. Theoretical and practical implications for understanding cross-cultural differences in self-presentation behaviors on social network sites were discussed.     

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.     

Generalized Strong Pseudoprime Tests and Applications

We describe probabilistic primality tests applicable to integers whose prime factors are all congruent to 1 mod r where r is a positive integer;r =  2 is the Miller–Rabin test. We show that if ν rounds of our test do not find n ≠  =  (r +  1)²composite, then n is prime with probability of error less than (2 r) ^− ν. Applications are given, first to provide a probabilistic primality test applicable to all integers, and second, to give a test for values of cyclotomic polynomials.     

Facebook use and academic performance among college students: A mixed-methods study with a multi-ethnic sample

This paper uses a mixed-methods approach to examine the relation between online academic disclosure and academic performance. A multi-ethnic sample of college students (N = 261; male = 66; female = 195; M age ≈ 22 years) responded to open-ended questions about their Facebook use. Thematic analysis revealed that over 14% of the Facebook wall posts/status updates (N = 714) contained academic themes; positive states were more frequent than negative and neutral states and students with lower GPAs expressed negative states more often. A path analysis suggested that academic performance may determine college students’ Facebook use, rather than the reverse. Implications for student support services are discussed.     

Differences in computer exposure between university administrators and CAD draftsmen

This study utilized an external logger system for onsite measurements of computer activities of two professional groups—twelve university administrators and twelve computer-aided design (CAD) draftsmen. Computer use of each participant was recorded for 10 consecutive days—an average of 7.9 ± 1.8 workdays and 7.8 ± 1.5 workdays for administrators and draftsmen, respectively. Quantitative parameters computed using recorded data were daily dynamic duration (DD) and static duration, daily keystrokes, mouse clicks, wheel scrolling counts, mouse movement and dragged distance, average typing and clicking rates, and average time holding down keys and mouse buttons. Significant group differences existed in the number of daily keystrokes (p < 0.0005) and mouse clicks (p < 0.0005), mouse distance moved (p < 0.0005), typing rate (p < 0.0001), daily mouse DD (p < 0.0001), and keyboard DD (p < 0.005). Both groups had significantly longer mouse DD than keyboard DD (p < 0.0001). Statistical analysis indicates that the duration of computer use for different computer tasks cannot be represented by a single formula with same set of quantitative parameters as those associated with mouse and keyboard activities. Results of this study demonstrate that computer exposure during different tasks cannot be estimated solely by computer use duration. Quantification of onsite computer activities is necessary when determining computer-associated risk of musculoskeletal disorders. Other significant findings are discussed.     

Assessment of C-band synthetic aperture radar data for mapping and monitoring Coastal Plain forested wetlands in the Mid-Atlantic Region,U.S.A.

Multi-temporal C-band SAR data (C-HH and C-VV), collected by ERS-2 and ENVISAT satellite systems, are compared with field observations of hydrology (i.e., inundation and soil moisture) and National Wetland Inventory maps (U.S. Fish and Wildlife Service) of a large forested wetland complex adjacent to the Patuxent and Middle Patuxent Rivers, tributaries of the Chesapeake Bay. Multi-temporal C-band SAR data were shown to be capable of mapping forested wetlands and monitoring hydroperiod (i.e., temporal fluctuations in inundation and soil moisture) at the study site, and the discrimination of wetland from upland was improved with 10 m digital elevation data. Principal component analysis was used to summarize the multi-temporal SAR data sets and to isolate the dominant temporal trend in inundation and soil moisture (i.e., relative hydroperiod). Significant positive, linear correlations were found between the first principal component and percent area flooded and soil moisture. The correlation (r²) between the first principal component (PC1) of multi-temporal C-HH SAR data and average soil moisture was 0.88 (p = < .0001) during the leaf-off season and 0.87 (p = < .0001) during the leaf-on season, while the correlation between PC1 and average percent area inundated was 0.82 (p = < .0001) and 0.47 (p = .0016) during the leaf-off and leaf-on seasons, respectively. When compared to field data, the SAR forested wetland maps identified areas that were flooded for 25% of the time with 63–96% agreement and areas flooded for 5% of the time with 44–89% agreement, depending on polarization and time of year. The results are encouraging and justify further studies to attempt to quantify the relative SAR-derived hydroperiod classes in terms of physical variables and also to test the application of SAR data to more diverse landscapes at a broader scale. The present evidence suggests that the SAR data will significantly improve routine wooded wetland mapping.     

Transforming a teacher education method course through technology: effects on preservice teachers’ technology competency

In this study, an instructional design model was employed for restructuring a teacher education course with technology. The model was applied in a science education method course, which was offered in two different but consecutive semesters with a total enrollment of 111 students in the fall semester and 116 students in the spring semester. Using tools, such as multimedia authoring tools in the fall semester and modeling software in the spring semester, teacher educators designed high quality technology-infused lessons for science and, thereafter, modeled them in classroom for preservice teachers. An assessment instrument was constructed to assess preservice teachers’ technology competency, which was measured in terms of four aspects, namely, (a) selection of appropriate science topics to be taught with technology, (b) use of appropriate technology-supported representations and transformations for science content, (c) use of technology to support teaching strategies, and (d) integration of computer activities with appropriate inquiry-based pedagogy in the science classroom. The results of a MANOVA showed that preservice teachers in the Modeling group outperformed preservice teachers’ overall performance in the Multimedia group, F = 21.534, p = 0.000. More specifically, the Modeling group outperformed the Multimedia group on only two of the four aspects of technology competency, namely, use of technology to support teaching strategies and integration of computer activities with appropriate pedagogy in the classroom, F = 59.893, p = 0.000, and F = 10.943, p = 0.001 respectively. The results indicate that the task of preparing preservice teachers to become technology competent is difficult and requires many efforts for providing them with ample of opportunities during their education to develop the competencies needed to be able to teach with technology.     

Cyberbullying among young adults in Malaysia: The roles of gender,age and Internet frequency

This study investigated the extent of young adults’ (N = 393; 17–30 years old) experience of cyberbullying, from the perspectives of cyberbullies and cyber-victims using an online questionnaire survey. The overall prevalence rate shows cyberbullying is still present after the schooling years. No significant gender differences were noted, however females outnumbered males as cyberbullies and cyber-victims. Overall no significant differences were noted for age, but younger participants were found to engage more in cyberbullying activities (i.e. victims and perpetrators) than the older participants. Significant differences were noted for Internet frequency with those spending 2–5 h online daily reported being more victimized and engage in cyberbullying than those who spend less than an hour daily. Internet frequency was also found to significantly predict cyber-victimization and cyberbullying, indicating that as the time spent on Internet increases, so does the chances to be bullied and to bully someone. Finally, a positive significant association was observed between cyber-victims and cyberbullies indicating that there is a tendency for cyber-victims to become cyberbullies, and vice versa. Overall it can be concluded that cyberbullying incidences are still taking place, even though they are not as rampant as observed among the younger users.     

Improvement of picture quality of high Xe content plasma display panels based on facing discharge suppression

This paper presents a waveform for driving a high-resolution plasma display panel (PDP) which uses a gas mixture of high Xe content. To prevent degradation of picture quality due to unstable discharges between two facing electrodes, the common electrode was biased at a negative voltage during the set-up period, and the data electrode was biased at a positive voltage during the sustain period. A pre-reset pulse was used before the first reset to reduce the reset voltage and to form a proper wall charge state for sustain discharges. This waveform could drive a 15% Xe 42 in. XGA (1024 × 768) PDP with the single-scan method. The measured black luminescence, peak luminescence, and contrast ratio were 0.45, 1490 cd/m², and 3310:1, respectively. The measured margin of the sustain voltage was better than ±10 V.     

Peri-Watchdog: Hunting for hidden botnets in the periphery of online social networks

In order to evade detection of ever-improving defense techniques, modern botnet masters are constantly looking for new communication platforms for delivering C&C (Command and Control) information. Attracting their attention is the emergence of online social networks such as Twitter, as the information dissemination mechanism provided by these networks can naturally be exploited for spreading botnet C&C information, and the enormous amount of normal communications co-existing in these networks makes it a daunting task to tease out botnet C&C messages.Against this backdrop, we explore graph-theoretic techniques that aid effective monitoring of potential botnet activities in large open online social networks. Our work is based on extensive analysis of a Twitter dataset that contains more than 40 million users and 1.4 billion following relationships, and mine patterns from the Twitter network structure that can be leveraged for improving efficiency of botnet monitoring. Our analysis reveals that the static Twitter topology contains a small-sized core sugraph, after removing which, the Twitter network breaks down into small connected components, each of which can be handily monitored for potential botnet activities. Based on this observation, we propose a method called Peri-Watchdog, which computes the core of a large online social network and derives the set of nodes that are likely to pass botnet C&C information in the periphery of online social network. We analyze the time complexity of Peri-Watchdog under its normal operations. We further apply Peri-Watchdog on the Twitter graph injected with synthetic botnet structures and investigate the effectiveness of Peri-Watchdog in detecting potential C&C information from these botnets.To verify whether patterns observed from the static Twitter graph are common to other online social networks, we analyze another online social network dataset, BrightKite, which contains evolution of social graphs formed by its users in half a year. We show not only that there exists a similarly relatively small core in the BrightKite network, but also this core remains stable over the course of BrightKite evolution. We also find that to accommodate the dynamic growth of BrightKite, the core has to be updated about every 18 days under a constrained monitoring capacity.     

Computer-based analysis of explicit approximations to the implicit Colebrook–White equation in turbulent flow friction factor calculation

The implicit Colebrook–White equation has been widely used to estimate the friction factor for turbulent fluid-flow in rough-pipes. In this paper, the state-of-the-art review for the most currently available explicit alternatives to the Colebrook–White equation, is presented. An extensive comparison test was established on the 20 × 500 grid, for a wide range of relative roughness (ε/D) and Reynolds number (R) values (1 × 10^?6 ? ε/D ? 5 × 10^?2; 4 × 10³ ? R ? 10⁸), covering a large portion of turbulent flow zone in Moody’s diagram. Based on the comprehensive error analysis, the magnitude points in which the maximum absolute and the maximum relative error are occurred at the pair of ε/D and R values, are observed. A limiting case of the most of these approximations provided friction factor estimates that are characterized by a mean absolute error of 5 × 10^?4, a maximum absolute error of 4 × 10^?3 whereas, a mean relative error of 1.3% and a maximum relative error of 5.8%, over the entire range of ε/D and R values, respectively. For practical purposes, the complete results for the maximum and the mean relative errors versus the 20 sets of ε/D value, are also indicated in two comparative figures. The examination results for error properties of these approximations gives one an opportunity to practically evaluate the most accurate formula among of all the previous explicit models; and showing in this way its great flexibility for estimating turbulent flow friction factor. Comparative analysis for the mean relative error profile revealed, the classification for the best-fitted six equations examined was in a good agreement with those of the best model selection criterion claimed in the recent literature, for all performed simulations.