Timothy D Bowman Dissertation Defense
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My presentation for my dissertation defence on Wednesday, May 20th, 2015, 2:00 p.m. in the Department of Information and Library Science, Room LI-00, School of Informatics and Computing, Indiana University Bloomington
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Timothy D Bowman Dissertation Defense
- 1. Investigating the Use of Affordances and Framing Techniques by Scholars to Manage Personal and Professional Impressions on Twitter Timothy D. Bowman, Ph.D. Candidate, Indiana University Research Professional, Université de Montréal
- 3. MOTIVATION
- 6. • Twitter use is increasing (Brenner & Smith, 2013) • Around 10% of scholars on Twitter with variation by field (Ponte & Simon, 2011; Rowlands et al., 2011) • 20% of scientific articles shared on Twitter (Haustein et al., 2014; Holmberg and Thelwall, 2014). TWITTER Brenner & Smith, 2013
- 7. • Few people examining impression management of scholars in Twitter • Studies of scholars tend to focus on social media in the classroom or on scholarly output (altmetrics) • Populations of scholars on Twitter tend to be limited LITERATURE GAP
- 9. The Presentation of Self in Everyday Life (Goffman, 1959) PRESENTATION OF SELF BACKSTAGE INFORMALTALK RELAXED ROLE BARRIER GIVE OFF GIVE DRAMATIC INTERACTION FRONT STAGE SIGNS PROPS B A R R I E R IMPRESSION MANAGEMENT Expressing certain information in order to impress certain ideas upon an audience during social interaction
- 10. Frame Analysis: An Essay on the Organization of Experience (Goffman, 1974) FRAME ANALYSIS FRAMERIM PRIMARY FRAME KEYING FABRICATION SIGNS/SYMBOLS BRACKETS ROLES EXPERIENCE ENGROSSABLES
- 11. TheTheory of Affordances. (Gibson, 1977) AFFORDANCE CONTEXT SOCIAL RULES EXPERIENCE
- 12. 1. In what ways do scholars utilize affordances to manage impressions on Twitter? 2. In what ways do scholars frame interactions to manage impressions on Twitter? 3. What are the differences in the use of framing strategies and affordances by scholars for managing the presentation of their professional and personal selves on Twitter? RESEARCH QUESTIONS
- 14. Phase One Online survey of full, associate, & assistant professors Phase Two Tweet categorization in Amazon’s Mechanical Turk (AMT) Phase Three Follow-up survey and tweet categorization with most active professors on Twitter RESEARCH METHODS
- 15. • 62 Association of American Universities (AAU) Member Schools (2 Canadian) • 16,862 Full, Associate, and Assistant Professors • 8 Departments – Anthropology, Biology, Chemistry, Computer Science, English, Philosophy, Physics, and Sociology PHASE ONE: SAMPLING
- 16. • 19 questions • Five sections • Matrix questions • Likert-scale questions • 8.5% response rate PHASE ONE: INSTRUMENT
- 17. χ2 (7, n=1,910) = 0.182, p = .0005, Cramér’sV = 0.182 PHASE ONE: TWITTER USE 50.0% 37.5% 36.9% 29.0% 27.5% 27.1% 24.3% 20.7% Computer Science English Sociology Anthropology Biology Philosophy Physics Chemistry 68% 32% NO YES (n=613) (n=1,297) (n=224) (n=299) (n=271) (n=169) (n=367) (n=144) (n=267) (n=169) (N=1,910) (N=1,910)
- 18. 45% 41% Non-White White PHASE ONE: TWITTER USE 38% 49% Male Female (n=615) (n=1,200) 39% 41% 25% 6 Years or Less 7 to 9 Years 10 Years or More (n=1,262) (n=196) (n=363) 44% 36% 28% 11% 35 and Under 36 to 45 46 to 60 61 and Over (n=271) (n=841) (n=517) (n=194) (n=229) (n=1,580) χ2 (2, n=1,821) = 0.217, p = .0005, Cramér’sV = 0.217) χ2 (3, n=1,823) = 0.125, p = .0005, Cramér’sV = 0.125. χ2 (2, n=1,824) = 0.066, p = .018, Cramer’sV = 0.18
- 19. PHASE ONE: SOCIAL MEDIA USE 0.4% 0.7% 0.9% 2.0% 2.7% 2.9% 4.8% 5.2% 5.7% 7.2% 7.3% 7.4% 15.4% 16.7% 21.5% 26.2% 27.8% 32.0% 50.2% 57.8% 69.9% Scilink Epernicus BioMedExperts.com MySpace Scribd SlideShare Tumblr Other Blogger Pinterest Mendeley Instagram WordPress Wikipedia Academia.edu ResearchGate YouTube Twitter Google+ LinkedIn Facebook(N=1,639)
- 20. 42% 29% 29% Professor (n=253) Associate (n=177) Assistant (n=178) PHASE ONE: TWITTER USERS 18.3% 18.3% 16.5% 16.3% 10.6% 8.0% 6.0% 6.0% English Computer Science Biology Sociology Physics Anthropology Philosophy Chemistry (n=112) (n=112) (n=101) (n=100) (n=65) (n=49) (n=39) (n=35)
- 21. PHASE ONE: TWITTER USERS 22% 28% 21% 15% 6% 3% 5% < 1 year 1 to 2 years 2 to 3 years 3 to 4 years 4 to 5 years 5 to 6 years > 6 years 62% 37% Male Female
- 22. 29% 42% 29% PHASE ONE: ACCOUNT TYPE BOTH PROFESSIONALPERSONAL
- 23. PHASE ONE: ACCOUNT TYPE 55% 22% 19% 25% 35% 28% 42% 44% 21% 49% 60% 41% 39% 37% 33% 25% 24% 29% 22% 34% 26% 34% 24% 31% Philosophy (n=33) Biology (n=93) English (n=102) Sociology (n=88) Chemistry (n=31) Computer Science (n=102) Anthropology (n=45) Physics (n=59) Personal Both Professional
- 24. PHASE ONE: AFFORDANCE USE 3% 6% 8% 8% 13% 14% 25% 19% 5% 5% 29% 26% 34% 34% 26% 34% 27% 80% 94% 93% 68% 68% 58% 58% 61% 53% 48% Add Photo Add Location Address Message At Mention Someone Use Hashtags Embed URLs Delete a tweet Favorite a tweet Reply to a tweet Retweet a tweet Mostly or Always Sometimes Rare or Never
- 25. PHASE ONE: AFFORDANCE USE 60.3% 52.1% 43.3% 40.8% 38.0% 24.9% 24.6% 23.5% 22.9% 19.8% 14.4% 8.2% 6.8% 5.1% 4.8% 2.5% 1.7% Profile Picture Bio information Apps Allowed Access to Twitter Privacy Settings Header Picture Twitter Sends Email Language Specified Twitter Connected to… Country Time Zone Theme Geo Tagging Twitter Sends Text Messages Twitter Personalizes Interface Widget(s) Created Phone Number Specified Sleep Settings 87.3% 87.3% 16.2% Professional Title Place of Work Post-nominal letters (e.g. Ph.D.)
- 26. PHASE ONE: FINDINGS • Age, academic age, department, and gender associated with having Twitter account • The majority of professors indicated using their Twitter account for both personal and professional communications • There were differences in perceived affordance use
- 27. Phase One Online survey of full, associate, & assistant professors Phase Two Tweet categorization in Amazon’s Mechanical Turk (AMT) Phase Three Follow-up survey and tweet categorization with most active professors on Twitter RESEARCH METHODS
- 28. • 445 Twitter accounts • 289,934 tweets collected • 75,000 tweets in AMT PHASE TWO: DATA COLLECTION
- 29. PHASE TWO: DATA COLLECTION Group Name AverageTweets per Day (TPD) Total Scholars in Group TotalTweets Collected Percentage of TotalTweets Tweets Used in AMT TEN (intense) 8 to 24 9 29,064 10.02% 7,518 NINE 5 to 8 8 25,863 8.92% 6,690 EIGHT 4 to 5 6 19,321 6.66% 4,998 SEVEN 3 to 4 10 24,532 8.46% 6,346 SIX 2.5 to 3 10 25,508 8.80% 6,598 FIVE 2 to 2.5 10 22,195 7.66% 5,741 FOUR 1.5 to 2 13 23,018 7.94% 5,954 THREE 1 to 1.5 29 43,831 15.12% 11,338 TWO 0.5 to 1 33 30,463 10.51% 7,880 ONE (infrequent) < 0.5 317 46,139 15.91% 11,935 445 289,934 100.00% *75,000 *Confidence Interval 0.4 at 99% Confidence Level 0.0 5.0 10.0 15.0 20.0 25.0
- 30. • 12,056 Human Intelligence Tasks (HIT) • 7 tweets per HIT • 1 control question per HIT • 3 Turkers per HIT PHASE TWO: INSTRUMENT
- 31. Anthro Bio Chem Comp Sci Eng Philo Phys Soc Average HASHTAGS 4.4% 5.5% 5.2% 5.2% 4.9% 4.6% 6.4% 7.4% 5.5% URLs 0.7% 1.2% 0.3% 1.1% 0.5% 1.7% 0.8% 1.1% 0.9% MENTIONS 11.6% 16.3% 12.9% 9.2% 13.4% 10.6% 13% 20% 13.4% RETWEETS 241 273 137 244 291 171 124 205 211 PHASE TWO: AFFORDANCES
- 32. 1.96 1.41 1.18 1.06 0.73 0.67 0.53 0.52 Philosophy English Anthropology Sociology Biology Computer Science Physics Chemistry 0.80 1.02 Female Male PHASE TWO: TWEET ANALYSIS Mean tweets per day
- 33. PHASE TWO: AMT RESULTS PERSONAL PROFESSIONAL UNKNOWN NON-ENGLISH TOTAL Full Agreement (3/3) 27,264 6,810 129 766 34,969 (47%) PartialAgreement (2/3) 19,403 15,692 1,993 262 37,355 (49%) NoAgreement 2,674 (4%) 96% agreement for 2 out of 3Turkers on all tweets
- 34. 17% 15% 67% 17% 28% 69% 56% 36% Hashtags URLs User Mentions Retweets Personal Professional χ2 (1, n=34,074) = 0.187, p = 0.0005, Cramer’sV = 0.187 χ2 (1, n=34,074) = -0.089, p = 0.0005, Cramer’sV = 0.089 χ2 (1, n=34,074) = 0.491, p = 0.0005, Cramer’sV = 0.491 χ2 (1, n=34,074) = 0.112, p = 0.0005, Cramer’sV = 0.112 PHASE TWO: AMT RESULTS
- 35. PHASE TWO: FINDINGS • URLs, retweets, and hashtags occurred more often in professional tweets • User mention was the only affordance to occur more in personal tweets • There were also differences across personal and professional tweets by academic title, age, gender, department and Twitter activity
- 36. Phase One Online survey of full, associate, & assistant professors Phase Two Tweet categorization in Amazon’s Mechanical Turk (AMT) Phase Three Follow-up survey and tweet categorization with most active professors on Twitter RESEARCH METHODS
- 37. • 95 Most Active Scholars on Twitter • 5 Tweets to Categorize – 2 Personal, 3 Professional PHASE THREE: DATA COLLECTION
- 38. • 6 questions • 5 tweets: 2 Personal, 3 Professional PHASE THREE: INSTRUMENT
- 39. PHASE THREE: AFFORDANCE USE 54.2% 42.4% 44.1% 42.4% 54.2% 32.2% 55.9% 30.5% 5.1% 10.2% 84.7% 84.7% 79.7% 78.0% 61.0% 61.0% 54.2% 13.6% 6.8% 3.4% Mentions URLs Retweets Hashtags Directed messages Punctuation, caps, etc. Media Emoticons Other Not used in this way Personal Professional
- 40. PHASE THREE: AFFORDANCE USE 25% 21% 6% 13% 8% 6% 2% 19% 77% 60% 32% 30% 23% 17% 11% 11% Description Profile Image Theme Header (banner) Image Location Colors Other Not used in this way Personal Professional
- 41. PHASE THREE: TWEET CATEGORIZATION PERSONAL PROFESSIONAL TOTAL AGREEMENT TURKERS (3/3) 102 153 255 SCHOLAR 44 125 169 43% Agreement 82% Agreement 69% Agreement OBSERVED TURKERS EXPECTED TURKERS Personal Professional Personal Professional Personal 44 28 Personal 29 43 Professional 58 125 Professional 73 110 Cohen’s Kappa = 0.26 Turker and Scholar Agreement Turker and Scholar Coding: Cohen’s Kappa PROFESSOR PROFESSOR
- 42. TURKER: Personal PROFESSOR: Professional TURKER: Professional PROFESSOR: Personal INCORRECTLY CATEGORIZED TWEET
- 43. CORRECTLY CATEGORIZED TWEET TURKER: Professional PROFESSOR: Professional #citsciWhat motivates you to take part in @the_zooniverse or citizen science projects. Help us find out!. http://t.co/2tth5RVpmN TURKER: Personal PROFESSOR: Personal @k_garten @bashartak @sig_chi we should just form CHI University and all hang out together all the time. #chi2013
- 44. PHASE THREE: FINDINGS • It is difficult for audience members to distinguish between personal and professional tweets • The framing of tweets are associated with specific affordances • Professional tweets are perceived as containing more hashtags, URLs and are retweets • Personal tweets are perceived as containing more user mentions
- 47. •Sample Population •Survey Response Rate/Design •Low Cohen’s Kappa LIMITATIONS
- 48. • Distinguishing between personal and professional tweets is difficult • Framing of tweets by audience members depends on many factors, one of which may be affordances • There are differences in tweeting behavior by various demographic characteristics • One of the largest studies on U.S. professors onTwitter to date • Unique use of Amazon’s MechanicalTurk • Provides foundation for altmetrics research • Social media use is a hot topic SUMMARY
- 49. 1.Analyze events in online social media 2.Utilize theories and methods from multiple disciplines 3.Use quantitative and qualitative methods FUTURE DIRECTION
- 50. Thank You Timothy D. Bowman, Ph.D.Candidate, Indiana University Research Professional, Université de Montréal
Hinweis der Redaktion
- WELCOME THANK YOU FOR ATTENDING AFFRODANCE, FRAMING, PERSONAL AND PROFESSIONAL IMPRESSIONS, SCHOLARS on TWITTER
- FOUR SECTIONS: MOTIVATIONS THEORIES METHODS & RESULTS IMPLICATIONS
- FIRST MOTIVAtiONS: TECHNOLOGY is UBIQUITOUS ASPECT OF DAY-TO-DAY LIFE ONE ASPECT OF TECHNOLOGY USE -> SOCIAL MEDIA SOCIAL MEDIA COMES IN A VARIETY OF PLATFORMS -> TWITTER, FACEBOOK, GOOGLE+, YOUTUBE, RESEARCHGATE, MENDELEY, ACADEMIA.EDU INTERESTING BECAUSE-> WE COMMUNICATE IN THESE CONTEXTS SUCCESSFULLY AND UNSUCCESSFULLY WE MIGRATE ACROSS PLATFORMS AND MAINTAIN VARIOUS PROFILES AND ENACT DIFFERENT ROLES WE SEEM TO HAVE DIFFERENT EXPEREINCES ACROSS THESE PLATFORMS I AM A SOCIAL MEDIA USER AND A SCHOLAR THERE IS GROWING LITERATURE ON SUBJECT
- FOUR EXAMPLES OF CONTROVERSIAL TWEETS WHEN I WAS MADE AWARE OF THESE TWEETS, I BEGAN TO CONCENTRATE MY FOCUS OF SOCIAL MEDIA USE TO SCHOLARS ON TWITTER WHAT MAY HAVE BEEN EMOTIOINAL OUTBURST OR FREUDIAN SLIP OFFLINE, NOW IS SUBJECTED TO VAST INVISIBLE AUDIENCES ON SOCIAL MEDIA ONE REASON THESE CAUSE CONTROVERSY IS BECAUSE THEY ARE MADE BY SCHOLARS SCHOLARS ARE HELD TO HIGH STANDARDS THEY ARE CONSIDERED REPRESENTATIVE OF THEIR DEPARTMENTS, UNIVERSITY, AND OF SCIENCE
- THESE TWEETS HAD REPRECUSSIONS FOR THE SCHOLARS The University of New Mexico censored Geoffrey Miller. His censorship included not allowing him to be on student admittance committees, assigning him with a faculty mentor, preventing him from teaching for a semester, and requiring that his work be monitored. He has since deleted this Twitter account. The University of Kansas suspended David Guth. His suspension lasted a semester in which he could not teach. Guth has since deleted this Twitter account. The University of Illinois at Urbana-Champaign rescinded their job offer to Steven Salaita. Salaita is suing the university administration and continues to tweet. In the most recent example, Saida Grundy, a sociology professor at Boston University, made what some referred to as racists comments and recently has publically apologized for her tweets. The university did not punish her, but the president of the university said the comments were in poor choice. These examples caused me to closely examine the current literature on Twitter to determine if anyone has examined this phenomena
- LITERATURE REVIEW REVEALED THAT THERE ARE A VARIETY OF STUDIES EXAMINING ACTS ON TWITTER IT WAS REPORTED THAT TWITTER USE IS INCREASING IN GENERAL POPULATION WHEN LOOKING AT SCHOLARS SPECIFICALLY, VARIETY OF STUDIES EXAMINING SMALL POPULATIONS WITH VARYING RESULTS RESEARCH INDICATES BETWEEN 2.5 AND 30 PERCENT SCHOLARS ON TWITTER, MOST REPORTING 7 TO 10 PERCENT THE GROWING AREA OF ALTMETRICS FOUND THAT 20% OF SCIENTIFIC ARTICLES PUBLISHED SINCE 2012 SHARED ON TWITTER
- FULL LITERATURE REVIEW AVAILABLE IN DISSERTATION, RELEVANT FINDINGS: GAP IN LITERATURE EXAMINING PERSONAL AND PROFESSIONAL COMMUNICATION ON TWITTER STUDIES OF SCHOLARS TEND TO FOCUS ON CLASSROOM OR MEASURE SCHOLARLY OUTPUT POPULATIONS OF SCHOLARS EXAMINED LIMITED TO FEW DISCIPLINES AND HAVE SMALL SAMPLE SIZES
- As I stated earlier, this work investigates impression management, framing, and affordance use in Twitter by introducing and employing a socio-technical framework to interpret the findings. This framework combines concepts from Erving Goffman’s self-presentation and frame analysis models with James Gibson’s (1977) conception of affordance to create a model that can be used to investigate social media events I will now describe these concepts and models and go over my research questions
- Erving Goffman’s impression management concept stems from his famous work The Presentation of Self in Everyday Life in which he uses dramaturgical concepts to describe face-to-face interaction Goffman is a well-known sociologists who was one of the first to examine communicative acts using a micro-social lens. Goffman used concepts such as actors, the stage, props, and symbols to detail the ways in which humans interact. He detailed the process of impression management, where an actor attempts to manage the information presented to others in order to maintain the impressions others have of them during a performance In Goffman’s work, an actor presents information to an audience while maintaining a role This presentation utilizes props, signs, and symbols to convey information The actors maintains the role during the presentation by giving and giving off information; Giving includes verbal communications and Giving off includes maintaining a front comprised of mannerisms and posture Goffman divided this communicative act into front and back stage regions. The front region includes the area in which the actor maintains formal speak and attire in keeping with her role for the audience. The backstage region includes the area where the actor can relax and take on an informal role Gofffman saw these regions as being physically separated so that the audience could not se the actor in the informal role. Although Goffman wrote this in the 1950s, he did utilize examples of technology; for instance he spoke about the need for television news anchors and radio hosts to maintain their front stage demeanor during a broadcast and that role breakage could occur when the broadcaster was unaware they were still being broadcast and talked informally. While Goffman tended to focus on a physical barrier between the front stage and backstage presentations, I am interpreting this as a barrier to information as others before me have done, where actors must navigate between a formal (or professional) and informal (or personal) presentation of self. Goffman’s impression management concept has been utilized to interpret both online and offline communicative acts across multiple disciplines. His work is held in high regards across multiple fields and several scholars have adapted this model.
- In his later work, Frame Analysis: An Essay on the Organization of Experience, Goffman introduced the concept of framing to describe the ways in which people make sense of activities in day to day life He defined a FRAME as embodying the social norms and rules that underlie social organization and noted that frames take into account the unique circumstances of the context and the features in which the interaction occurs FRAMES CAN BE MODIFIED THROUGH KEYING OR FABRICATION KEYING: where a frame is adapted and used to understand a circumstance in context. EXAMPLE: PRACTICE FOR PRESENTATION FABRICATION: Fabrications occur when the persons putting on the act purposively try to deceive others who are watching or participating that one thing is occurring when it is actually not. EXAMPLE: EMAIL REQUESTING CREDENTIALS TO BANK ACCOUNT Frames enforced by CONTEXT in which they occur and EXPERIENE and EXPECTATIONS of the persons framing the act in question. Frames can be ENGROSSING , such that one loses track of events occurring outside of the framed event. They are reinforced by SIGNS and SYMBOLS within the context of the frame itself. Frames can also be BRACKETED by signifiers, such that one knows when there is a transition in the event or when the event is starting or finishing. Just as with Goffman’s earlier work on self-presentation, Goffman also noted that it is important for roles to be maintained throughout the framed event; otherwise a frame break can occur causing the event to no longer be understood under the current definitions. After examining the literature on frame analysis, it was apparent that there were no studies looking at this phenomenon in the context of Twitter.
- The last component of my model utilizes the concept of AFFORDANCE as introduced by James Gibson. Gibson is a well-known psychologist who questioned the conception that animals first notice the physical characteristics of objects in their environment. Gibson introduced the concept of affordance to describe the functional attributes of an object, as separate from their physical characteristics, available in a specific context He noted that a person incorporates the CONTEXT of the object into how they determine what affordances are available and how they might use them. It this example, we can see that a tree affords different functionalities to the various animals who come across it. A tree will afford a squirrel a source of nutrients, a monkey a hiding place, a human a source of materials, and a bird a nesting location. After reviewing the literature it found that there were many discussions of affordances in social media. However, there were no studies found examining the affordances of Twitter in the way employed in this work …and only one that combined Goffman’s frame analysis with affordances to examine presence on mobile phones. In addition, to my knowledge this is the first study to create a list of the specific affordances available in the Twitter environment.
- The research questions are based on these motivations and theories: examine the affordances used to manage impressions by scholars A frame analysis of tweets made by scholars as they communicate on Twitter And differences in framing strategies and affordance use by scholars in personal and professional tweets. I will now discuss the methods used and results obtained in this work.
- This dissertation was conducted in three phases from January to October, 2014 These three phases and the results will now be described sequentially.
- The first phase included conducting a survey of professors residing in eight departments at 62 universities
- This first phase included conducting a survey of full, assistant and associate professors from 62 Association of American University schools from eight departments The sample was collected by harvesting the available information from the eight departmental websites at each school. These schools included 34 public universities, 26 private universities and 2 Canadian universities The eight departments were chosen to represent both the natural and social science. The data was collected by collecting the name, affiliation, professional title, and email address of all relevant professors Any faculty member who was not a full, associate, or assistant professor at the university was not included The final sample contained 16,862 individuals.
- The survey instrument was tested by one professor and two graduate students After three iterations of design changes, the survey was pilot tested by one faculty member and two students The final survey contained 19 questions broken up into five sections and used logic to control the flow of the survey based on responses The first section asked the participant if they had a Twitter account. If the participant answered NO, they were only given a question about other social media use and basic demographic questions found in the fifth section. The second section asked the participant how many accounts they had, how long they had been on Twitter, and .how they would classify the type of account The third section asked participants about affordances they used while tweeting The fourth section asked participants about other affordance use within the Twitter environment Out of the 16,665 invitations delivered, 1,910 participated for a participation rate of 8.5%. For a survey this large, the literature on survey design argues that a response rate of 10 to 15% is adequate.
- 32% of the scholars responding to this survey indicated they had at least one Twitter account. As mentioned earlier, this is above the 7 to 10 percent range typically reported. When examining the total respondents by department, it was found that professors from computer science had the highest proportion of respondents with Twitter accounts. Whereas professor from Chemistry reported the lowest proportion.
- There were significant differences found by academic age, age and gender. Respondents who had been at their position 10 years or more had less Twitter accounts than those who’d been at their position for a shorter amount of time. With regards to actual age, younger scholars were found to have a higher proportion of Twitter accounts than older scholars. Gender differences found that females had a higher proportion of accounts, but that more males were included in this study based on overall response. When comparing ethnicity it was found that there was small difference between white and non-white participants, but this was not significant.
- With regards to social media use, it was found that most scholars reported having a Facebook account, followed LinkedIn and GooglePlus When examining social media tools related to academic use, it was found that ResearchGate and Academia.edu were used much more than Mendeley or other platforms.
- When examining only those who reported having at least one Twitter account, it was found that both English and Computer Science had the highest number of users. Chemistry and philosophy professors comprised the lowest number of users. When looking at academic titles, there was an even distribution of assistant and associate professors, while the majority were full professors
- Another aspect examined was time on Twitter. Less than 15% of the participants had been on Twitter four years or longer, while it was found that 71% of respondents had been on Twitter for three years or less. Finally it was found that there were more males in the final sample than females.
- As mentioned earlier, one of the survey questions asked participants who had Twitter accounts how they would classify their accounts 42% of respondents classified their account as both personal and professional
- When examining account type differences by department, it was found that English professors reported the highest percentage of both personal and professional accounts. Philosophy professors had the highest percentage of personal-only accounts And both computer science and sociology professors had the highest percentage of professional only accounts
- When looking at affordance use, overall respondents indicated that they felt they most often retweeted a tweet. They indicated that they used URLs mostly or always 8% of the time They indicated that they used hashtags mostly or always 6% of the time They indicated that they mentioned users mostly or always 3% of the time.
- Finally, participants were asked about other Twitter affordance use and information they added to their profiles Users indicated that they had added a profile picture, bio information more than any other affordances With regards to bio information, a majority of participants added both their professional title and place of work to their Twitter bios.
- The important findings from phase one include: the demographic characteristics have a significant association with having a Twitter account the majority of professors indicate they use their account in both a personal and professional manner professors indicated differences in affordance use These findings provide a description of the types of scholars using Twitter and helps address both research questions one and three regarding affordance use and personal and professional communication by formulating a baseline of perceived use of affordances in Twitter and by establishing that professors utilize Twitter both personally and professionally
- The second phase started in May, 2014 when the sample of tweets was collected and was finished in July, 2014 when the final Turk tasks were completed.
- The second phase included randomly selecting tweets from 445 Twitter accounts of scholars identified from the survey in phase one. A PHP script was used to collect the most recent 3,200 tweets from each scholar’s account A total of 289,934 tweets were collected from a total of 585,879 tweets made A random sample of 75,000 tweets were selected for use in Amazon’s Mechanical Turk
- To select the 75,000 random tweets for inclusion in Amazon’s Mechanical Turk, a visual inspection was made of the data The tweets were first organized by average tweets per day by using the Twitter account date and the total number of tweets. The visualization of the data indicated no clear delineations, so a stratified sampling technique was used to collect the tweets for Amazon’s Mechanical Turk The professors were divided into 10 groups ranging from infrequent to intense Twitter use. The total number of tweets for each group was determined and then the percentage of overall tweets was calculated. This percentage of total tweets was multiplied by 75,000 and the resulting total was used to gather the tweets from each group.
- For phase the phase two instrument, Human Intelligence Tasks were created in Amazon’s Mechanical Turk application using HTML and JavaScript A total of 12,056 HITs were created containing up to 7 tweets per HIT for categorization as either PERSONAL, PROFESSIONAL, UNKNOWN, or NON-ENGLISH The categories of UNKNOWN and NON-ENGLISH were included after the sample was found to contain tweets that included nothing but punctuation or were not written in English For data integrity, a control tweet was added to each HIT that asked the Turker to categorize the tweet in a specific way There were 3 Turkers assigned to each HIT; each Turker was required to have completed at least 10,000 HITs and have an approval rating of at least 99% to be qualified for the task Turkers were paid $0.10 per hit for an average of approximately $5.00 per hour
- An analysis was made of all 289,934 tweets collected It was found that there were differences in affordance use by department with regards to hashtags, URLs, user mentions, and retweets
- It was also found that there differences by department in mean tweets per day Philosophy professors had the highest mean tweets per day, whereas chemists had the lowest There were also differences in gender as males were found to tweet more than females
- The results of the categorization found that there was a 47% agreement by all turkers on classification of tweets. Whereas there was no agreement on only 4% of all tweets
- The personal and professional categories were then examined for affordance use. For this phase of the analysis, only the full agreement set of tweets were examined where 3 out of 3 Turkers agreed There were significant differences between all four of the main affordances found in the personal and professional tweets. The only affordance found to exist more in personal tweets was the user mention. The largest difference between personal and professional tweets was the use of URLs.
- The important findings from phase two include: the examination of affordance use across the personal and professional tweets emphasized some trends that help shed light on the framing behaviors of scholars in each category of tweet URLs, retweets, and hashtags occurred more in professional tweets User mentions occurred more in personal tweets While not shown due to time constraints, there were also significant associations found between affordance use and academic title, age, gender, department and Twitter activity across the personal and professional tweets These findings address research question two regarding the framing behaviors of scholars by identifying differences in affordance use across personal and professional tweets
- The third phase was completed in October, 2014
- For this phase, the 95 most active tweeters as identified in phase two were sent a follow up survey. 5 publically available tweets were collected from the full agreement result set from phase two of this work There were 2 personal and 3 professional tweets randomly selected as categorized by the Turkers
- The survey instrument was created using Qualtrics software. The survey consisted of 5 questions regarding affordance use when communicating personally and professionally… and one question asking them to categorize 5 of their own publically available tweets as either PERSONAL or PROFESSIONAL The survey instrument was tested by one professor and two graduate students It was sent to the the 95 professors on October 27, 2014 There were 57 of the 95 scholars who completed this survey for a response rate of 63%
- Regarding the questions about affordance use between personal and professional tweets, Respondents indicated that they used all of these tweet-based affordances more in professional tweets than personal tweets … except for media and emoticons
- Similarly, these other Twitter affordances were all chosen as being used more professionally than personally
- With regards to the categorization of tweets, it was found that there was an 82% agreement of professional tweets, but only a 43% agreement of personal tweets A Coehn’s Kappa was run and a low rate of inter-rater reliability was found between the Turkers and the scholars. Overall there was only a 69% agreement of tweet categorization
- In the first example, the scholar tweets about a fun fact and talks about the CPI empathy scale and a question about showers and bathtubs. Here 3 turkers categorized this as personal, whereas the scholar categorized it as professional. In the second example, the scholar tweets about an academic article and the paywall system. Here the turkers categorized this as professional, whereas the professor categorized it as personal.
- Examples of correctly categorized tweets include a tweet that discuses citizen science projects and a tweet discussing hanging out at a conference. Each of these tweets were categorized in the same way by both turkers and professors.
- These findings address all of the research questions by examining differences in perceived affordance use by the most active Twitter users between personal and professional tweets the data establishes that it is difficult for audience members to distinguish between personal and professional tweets and that framing of professional tweets are associated with more affordance use of hashtags, URLs and retweets\ and that framing of personal tweets are associated with utilizing user mentions
- I would like to now discuss the implications of this work
- Introduced a socio-technical model combining Goffman’s impression management and frame analysis concepts with Gibson’s affordance concept Used to investigate scholarly communications on Twitter Goffman and Gibson both described technology when introducing their concepts My contribution is to use this lens to interpret events in social media in a productive way
- Sample could be biased toward social media use The survey response was low, but near expected response rate definitions of large scale web surveys There was a low Cohen’s Kappa for the categorization of tweets in phase three but it was expected