Why doesn't Twitter allow tweet-based protection?
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2 418 ARTICLES Peters Mazarakis tweeting at scientific conferences: Men are different, and women too Using tweets at the Science 2.0 conferences in 2014 and 2015, the gender-specific behavior of tweeters was examined in Kiel Isabella Peters and Athanasios Mazarakis Peters Mazarakis Introduction Social Media platforms, such as Twitter, are assigned to the area of microblogs and allow short messages to be sent, including photos or videos. They are not only very popular in the private sphere, but also researchers twittering more and more frequently, especially at scientific conferences 1. A survey among researchers has shown that 80% of those surveyed are at least familiar with Twitter, but only 13% use Twitter regularly 2. At scientific conferences, tweets are mainly used to share information and reports from the presentations with other Twitter users and to make the content known beyond the physical boundaries of the conference location. 3. The Twitter functions are often used for presentations to go into more detail, to ask questions, to refer to further information via links and to involve both the speaker and other Twitter users in the process. Studies have shown that the intensity of Twitter use depends on the disciplinary origin of the users 4 and that the various scientific disciplines use Twitter for different activities, 1 Rooney Ferris, L., & O Connor, M. (2015). It s Just like Passing Notes in Class A Content Analysis of the Use of Twitter at # asl2015. To Leabharlann, 24 (2), Van Noorden, R. (2014). Online collaboration: Scientists and the Social Network. Nature, 512, Mahrt, M., Weller, K., & Peters, I. (2014). Twitter in Scholarly Communication. In K. Weller, A. Bruns, J. Burgess, M. Mahrt, & C. Puschmann (Eds.). Twitter and Society (pp). New York, NY: Peter Lang. 4 Siegfried, D., Mazarakis, A., & Peters, I. (2014). Use of social media services in economics. Report on the results of an online survey among scientists working in economics at German universities and research institutions. An empirical study within the framework of the Leibniz Research Association Science 2.0. URL: e.g. For example, economists tend to share links while biochemists use retweets more often. 5. An important question relates to the latter aspect: In addition to the general analysis of the content of tweets in the context of scientific conferences, we are interested in the following study whether gender-specific differences can also be determined here. In scientific communication, gender differences in behavior (e.g. publishing, citing) and perception (e.g. attribution of competencies) have already been identified in numerous areas. For the traditional scientific communication via journal articles it could be shown, for example, that men publish more articles on average than women. However, the discrepancy is not the same in all disciplines. Articles in which women play central author roles (e.g. first author) are also cited less frequently.6 Women in the medical and scientific disciplines are listed more often as authors if they have assumed responsibility for the experiments reported in the article, and they tend not to be mentioned if they had taken on other roles in the scientific production cycle (e.g. writing articles or designing experiments) 7. Video contributions with female researchers are discussed more emotionally than those of male lecturers 8. Scientist 5 Holmberg, K ., & Thelwall, M. (2014). Disciplinary Differences in Twitter Scholarly Communication. Scientometrics, 101 (2), Larivière, V., Ni, C., Gingras, Y., Cronin, B., & Sugimoto, C. R. (2013). Bibliometrics: Global gender disparities in science. Nature, 504, Macaluso, B., Larivière, V., Sugimoto, T., & Sugimoto, C. R. (2016). Is Science Built on the Shoulders of Women? A Study of Gender Differences in Contributorship. Academic Medicine, 91 (8), doi: / ACM Sugimoto, C. R., & Thelwall, M. (2013), Scholars on soap boxes: Science communication and dissemination in TED videos. Journal of the American Society for Information Science & Technology, 64 (), doi: / asi
3 Peters Mazarakis PROFESSIONALS 419 on academic social networking sites, e.g. B. Mendeley, are perceived to be more attractive than their male colleagues through their profile pictures, but also less competent 9.Women make more use of Twitter functionalities (e.g. retweets, use of hashtags) than men when using Twitter for professional purposes, who use these functionalities more often for private tweets 10. Various studies have come to different results on the intensity of use by female and male Twitter users: No gender-specific differences have been found for researchers 11, although female tweets generally post tweets 12 it is argued that these gender inequalities are due to the fact that fewer women are to be found in both science and Twitter. While the former is true 13, the hypothesis for the latter aspect cannot be easily confirmed for the general Twitter population. Pew Internet Research 14 found that 21% of female and 24% of male Internet users have a Twitter account. Further studies show a proportion of male twitterers ranging from 45% (n = users) 15 to 71.8% (n = users) 16. If you only look at researchers, the proportion of male twitterers varies between 28% (n = users ) 17 and 61.4% (n = users) 18, 9 Tsou, A., Bowman, T., Sugimoto, T., Lariviere, V., & Sugimoto, C. (2016). Self-presentation in scholarly profiles: Characteristics of images and perceptions of professionalism and attractiveness on academic social networking sites. First Monday, 21 (4). doi: /fm.v21i Bowman, T.D. (2015). Differences in personal and professional tweets of 11 Ibid. 12 Burger, J. D., Henderson, J., Kim, G., & Zarrella, G. (2011). Discriminating 13 Larivière, V., Ni, C., Gingras, Y., Cronin, B., & Sugimoto, C. R. (2013). Bibliometrics: Global gender disparities in science. Nature, 504, Burger, J.D., Henderson, J., Kim, G., & Zarrella, G. (2011). Discriminating 16 Mislove, A., Lehmann, S., Ahn, Y.-Y., Onnela, J.-P., & Rosenquist, J. N. (2011). Understanding the demographics of Twitter users. In Proceedings of the International AAAI Conference on Web and Social Media, Fifth International AAAI Conference on Weblogs and Social Media, Barcelona, Spain: URL: Bowman, T. D. (2015). Differences in personal and professional tweets of 18 Ke, Q., Ahn, Y.-Y., & Sugimoto, C. R. (2016). A systematic identification and analysis of scientists on Twitter. URL: where a gender can only be determined for 15% 19 to 71.9% 20 of researchers' Twitter accounts on the basis of profile information. Female researchers publish more unambiguous gender-specific characteristics on Twitter profiles 21, which could also be confirmed for blogs 22. The different proportions of male and female twitterers result, for example, from different data sets and different methods of gender identification (e.g. self-reports for surveys 23 or automatic procedures 24). It can be assumed that conservative methods of determination, such as self-disclosure and manual classification of Twitter users, tend to increase the probability of receiving correct information on gender. The present study continues the research on gender-specific differences in Twitter use based on a case study. In particular, the content published in the tweets is dealt with and asked whether typically female and typically male characteristics can be determined for tweets. Database and results The Science 2.0 Conference 25, which took place on March 26th and 27th in 2014 and on March 25th and 26th in 2015, serves as the basis for the tweet analysis. In both years the conference location Hamburg was well attended with approx. 200 visitors on site and between 100 to 200 other spectators from afar, who were connected via livestream. A total of tweets containing the hashtag for the conference #sci 20conf could be collected for both years. In order to analyze the gender-specific content of tweets, all authors of the tweets were subjected to a manual check. This check was carried out conservatively, i.e. only if the user name and / or information from the profile allowed a reliable assignment to a gender, were 19 Ibid. 20 Ibid. 21 Burger, J. D., Henderson, J., Kim, G., & Zarrella, G. (2011). Discriminating 22 Herring, S.C., & Paolillo, J.C. (2006). Gender and genre variation in weblogs. Journal of Sociolinguistics, 10 (4), Bowman, T.D. (2015). Differences in personal and professional tweets of 24 Among others: Ke, Q., Ahn, Y.-Y., & Sugimoto, C. R. (2016). A systematic identification and analysis of scientists on Twitter. URL: abs /
4 420 PROFESSIONALS Peters Mazarakis Figure 1: from a woman Figure 2: from a woman Figure 3: from a man Figure 4: Example: tweet from a man the tweets of the author included in the analysis. In this way, 912 tweets could be identified for further analysis. The remaining contributions were not taken into account because they were either written by an institution or the gender could not be clearly recorded from the Twitter profile. As we focus exclusively on the content of the tweets and their gender-specific characteristics in this study, no further information on the authors of the tweets was collected. 487 tweets (53.4%) could be assigned to men and 425 tweets (46.6%) to women, so that it was possible to continue working with an almost balanced ratio between gender-specific tweets. A tried and tested categorization scheme for tweets at scientific conferences was used as the basis for determining the tweet content 26,27. This schema consists of three classes with associated sub-categories: the purpose of a tweet (e.g. sharing resources or organizational announcements), the link target of an existing URL (e.g. PowerPoint presentation or conference website) and the thematic content of a tweet ( e.g. open science or scientific methods). The evaluation using a log-linear model 28 led to a statistically significant result with χ² (5) = 60.95, p <.000 for the purpose of a tweet. To be able to interpret this result more precisely, separate chi-square tests were carried out. It can be shown that men sent tweets with links more often than women (χ² (1) = 21.61, p <.000). In addition, men tweeted more often about the conference conditions (χ² (1) = 6.83, p = .014), regardless of whether these were positive or negative comments. By contrast, women tweeted about conference content statistically significantly more often (χ² (1) = 26.65, p <.000) than men. The shared links also show statistically significant gender differences (χ² (8) = 33.51, p <.000). Men posted the conference website statistically significantly more often than women (χ² (1) = 8.93, p = .004), as well as presentation slides (only tweeted by men!) And other websites (χ² (1) = 9.90, p = .002 ). Finally, the analysis of the content of the tweets revealed gender differences (χ² (9) = 25.73, p <.001). The tweets from male senders contained statistically significantly more contributions to other conferences and further academic events than those from women (χ² (1) = 7.13, p = .011). Women, on the other hand, were more willing to communicate when it came to the topics of open science and open data (χ² (1) = 6.41, p = .014) and big data (χ² (1) = 6.92, p = .012) and when discussing terms and concepts (χ² (1) = 8.22, p = .006). Conclusion The present results give a first impression that communication is based on wis 26 Mazarakis, A., & Peters, I. (2015). Tweets and Scientific Conferences: The use Case of the Science 2.0 Conference. In Proceedings of 2nd European Conference on Social Media, Porto, Portugal (pp), Reading, UK: Academic Conferences and Publishing International Limited. 27 Mazarakis, A., & Peters, I. (2015). Science 2.0 a. Conference Tweets: What? Where? Why? When? Electronic Journal of Knowledge Management, 13 (4), Field, A. (2009). Discovering Statistics Using SPSS. London: SAGE.
5 422 SPECIALIST ARTICLES Peters Mazarakis s scientific conferences is also very complex and shaped differently by the sexes. The two samples from the Science 2.0 conferences in 2014 and 2015 show that gender differences in the communication content can be derived from the tweets. The results of this special use case with a small number of cases are thus similar to those that were also recorded for technology-supported communication in general 29. At the Science 2.0 conferences, men tweet additional content and websites that go beyond the conference content. Tweets from female twitterers, on the other hand, were primarily concerned with what was happening directly at the conference. Men and women therefore play different roles during scientific tweeting and pursue different goals with their tweets (e.g. women report and men refer to their own texts). There can be various reasons for this: On the one hand, this may be due to the analyzed target group of researchers (or at least users interested in scientific conferences) and their motivations for use. It has been shown that Twitter is more likely to be used in a professional academic context for exchanging ideas with peers, publishing professionally relevant information, establishing contact 30 and in science communication 31. On the other hand, certain expectations are made of the researcher as a person (e.g. students often do not expect to find lecturers on social network platforms 32 or researchers have to adhere to the institution's social media guidelines) that influence Twitter behavior can. Another explanation may be found in the researchers' expectations of the outcome of their Twitter activity. A study on blogs 33 showed that men in particular publish articles more frequently if they can expect something in return (e.g. higher visibility of their own research results or of themselves) or an improvement in their reputation as a result. Women are more likely to use blogs to present themselves, but in the sense that they reveal more personal content. Even if the motivation to use and expectations of blogs and tweets do not match one-to-one, we still get an indication of how the differences in the tweet content come about and in which direction further research could be carried out. The results of the present and similar studies can be of interest to the organizers of scientific conferences and their internet-based community management in order to be able to respond more precisely to the needs of the users (e.g. to initiate discussions or to bundle further information on a platform). In addition, these findings have, especially serious, implications for all tweet-based evaluation procedures, e.g. B. in the area of altmetrics.The hypothesis still to be checked here is that if women share fewer resources, mostly male relevance ratings flow into the altmetrics (for example, if a tweet with a link to a scientific article is viewed as a relevance rating, as is currently the case with commercial altmetrics providers is the case) and thus contain a systematic bias in favor of men. In order to be able to make conclusive statements, such studies must of course be expanded and reliable methods developed that take over the time-consuming, manual analysis of the tweet content and thus enable more extensive studies and data sets. 29 Herring, S.C. (1996). Two variants of an electronic message scheme. In Herring, S. (Ed.), Computer-Mediated Communication: Linguistic, Social, and Cross-Cultural (pp). John Benjamin Publishing: Amsterdam / Philadelphia, PA. 30 Van Noorden, R. (2014). Online collaboration: Scientists and the Social Network. Nature, 512, Siegfried, D., Mazarakis, A., & Peters, I. (2014). Use of social media services in economics. Report on the results of an online survey among scientists working in economics at German universities and research institutions. An empirical study within the framework of the Leibniz Research Association Science 2.0. URL: 32 Sugimoto, C., Hank, C., Bowman, T., & Pomerantz, J. (2015). Friend or faculty: Social networking sites, dual relationships, and context collapse in higher education. First Monday, 3 (2). doi: /fm.v20i Lu, H.-P., & Hsiao, K.-L. (2009). Gender differences in reasons for frequent blog posting. Online Information Review, 33 (1), doi: / Prof. Dr. Isabella Peters Head of the Web Science Working Group Technical Faculty Institute for Computer Science at the Christian-Albrechts- University (CAU) in Kiel and ZBW Leibniz Information Center for Economics, Kiel Dr. Athanasios Mazarakis Research Associate Postdoc Working Group WebScience Christian-Albrechts-Universität zu Kiel (CAU)
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