We analyze the publication co-authorship network of an iSchool faculty community using ???Social Identity Theory??? as the theoretical lens. Initially, we discuss the need for a theoretical framework to analyze and interpret social network data. Then, we find out the patterns in the levels of interaction happening within the faculty community at an inter-group level. We grouped faculty members into different clusters according to several parameters such as their educational backgrounds, affiliations with research centers/labs, and h-indices. We based our analysis on this classification and we try to understand the relationship among social identity, group affiliation and academic collaborations. We conclude with the remarks that one could avoid idiosyncratic ways of interpreting social network data by using a proven theoretical lens like ???Social Identity Theory??

Karunakaran, Arvind

Khabsa, Madian

Kim, Hyun-Woo

English

IDEALS @ Illinois

iSchools and Social Identity ??? A Social Network Analysis

We analyze the publication co-authorship network of an iSchool faculty community using ‘Social Identity Theory’ as the theoretical lens. Initially, we discuss the need for a theoretical framework to analyze and interpret social network data. Then, we find out the patterns in the levels of interaction happening within the faculty community at an inter-group level. We grouped faculty members into different clusters according to several parameters such as their educational backgrounds, affiliations with research centers/labs, and h-indices. We based our analysis on this classification and we try to understand the relationship among social identity, group affiliation and academic collaborations. We conclude with the remarks that one could avoid idiosyncratic ways of interpreting social network data by using a proven theoretical lens like ‘Social Identity Theory’Submitted by Heekyung Choi (hchoi8@illinois.edu) on 2010-04-05T14:09:23Z
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iSchools and Social Identity – A Social Network AnalysisArvind KarunakaranThe Pennsylvania State University,College of Information Sciences & Technology, University Park, Pennsylvania-16802, USA1-814-206-4189axk969@psu.edu Hyun-Woo KimThe Pennsylvania State University,College of Information Sciences & Technology, University Park, Pennsylvania-16802, USA1-814-360-0992Hxk263@psu.edu Madian KhabsaThe Pennsylvania State University,Department of Computer Science, University Park, Pennsylvania-16802, USA1-814-321-7666mxk479@psu.edu  ABSTRACTWe analyze the publication co-authorship network of an iSchool faculty  community  using  ‘Social  Identity  Theory’  as  the theoretical  lens.  Initially,  we discuss  the need  for a  theoretical framework to analyze and interpret social network data. Then, we find out the patterns in the levels of interaction happening within the  faculty  community  at  an  inter-group  level.  We  grouped faculty  members  into  different  clusters according  to several parameters  such  as  their  educational  backgrounds,  affiliations with research centers/labs,  and  h-indices. We based our analysis on  this  classification  and  we try to understand  the relationship among  social  identity,  group  affiliation  and  academic collaborations.  We  conclude  with  the  remarks  that  one  could avoid idiosyncratic  ways  of  interpreting social  network data  by using a proven theoretical lens like ‘Social Identity Theory’, Categories and Subject DescriptorsJ.3 [Social and Behavioral Sciences]: Psychology, SociologyGeneral TermsTheory, MeasurementKeywordsSocial  Identity,  iSchool,  Social  Network  Analysis,  Academic Collaboration1. INTRODUCTION‘Social  identity  theory’ [7]  was  originally  developed  to understand inter-group behavior and discrimination. According to this theory, an individual derives part of his self-concept from the groups  that the  individual  is  affiliated  with.  Thus,  the  social identity perspective is in part a critique of individualistic notions of  self,  and  argues  that  individual’s  social  identity  is  a  self-concept derived from the membership with social groups [4], [5]. The behavior of the individual is triggered by the social context and the individual’s perceived affinity with a specific group  that has  a  significant  influence  on  the  behavior.  Also,  other  sub-theories  within  the social  identity  perspective  such  as  the  self-categorization  theory [8]  and  the  social  categorization  theory [9] argue that individuals tend to continuously bracket themselves and  others  into  categories  and  in  this  process,  they  identify themselves with certain groups and not identify with certain other groups. This identification is critical, and helps them to develop a coherent  self-image  and  self-esteem  [7].  However,  this  does create  a  bias,  since  such  categorizations  would  lead  to  the formulation  of  in-groups  and  out-groups,  the  former  they increasingly  identify  with,  while the latter, they do  not  identify with.[7],[8].On  an  interdisciplinary  research  setting  like  iSchools  [2], the concept of social identity plays a very important role. One of the distinguishable characteristics of an iSchool  would be its faculty body  consisting  of  those  with  diverse  educational  and professional  backgrounds;  their  disciplines  include  computer science, information science, management science, organizational behavior,  physics,  cognitive  science,  sociology,  cultural anthropology,  social  psychology, etc.  Thereby,  the  individual faculty member’s exclusive identification with a specific group—depending  on  their  perceived  self-categorization and  social  categorization—might  influence  their  research  collaborations, which  could  in-turn impact  on  the climate  of  inter-disciplinary research within iSchools.  However, past studies  of  publication collaboration  in terms of a social network analysis have a tendency to overlook the influence of  social  identity.  A few studies did use the term social identity [10], but they did not use the theoretical framework put forth by Tajfel and Turner [7].In  this  poster,  we  adopt social  identity  theory as  our  lens  to analyze a publication collaboration network. We take an instance of  a  particular  iSchool  and  analyze  the  influence  of  social  identity and self categorization over the publication collaboration among  its  faculty  members  by  using  social  network  analysis techniques.2. RESEARCH METHOD2.1 Social Network AnalysisThe term “Social Network” initially started out as a metaphor for complex relationships or the sets of complex relationships among different entities at different scales. For example, the nature of  a relationship  between  an  individual  and  a  community  can  be illustrated and explained as a social network. Similarly, the nature of a relationship among different organizations forming a cartel to Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish,  to  post  on  servers  or  to  redistribute  to  lists,  requires  prior specific permission and/or a fee.Conference’04, Month 1–2, 2004, City, State, Country.Copyright 2004 ACM 1-58113-000-0/00/0004…$5.00.indulge  in  a  trade  agreement,  and the  complex  web  of relationships among countries involved in a nuclear deal could all be as well illustrated as social networks. With the advent of  some visualization tools  for social  networks such as Netdraw and Pajek, the field of social network analysis is picking  up  its  momentum.  Also, a  lot  of  researchers  in  many disciplines are finding it very useful so that they make use of the concepts  of  social  network  analysis  and  adopt  it  into  their research nowadays. By leveraging the visualization capabilities offered by these tools, we  try  to  understand  the  effects  of  social  identity  on  clique  behavior [6] at an “inter-group” level.2.2 Data ProcessingWe followed these techniques to extract  and process  data  from different sources.2.2.1 Data from CiteSeerCiteSeer is a public search engine and digital library for scientific and  academic  papers.  We  chose  CiteSeer  because  it  contains publications information in  a  highly  structured format.  Besides, the  system  accepts logical  operators  in  a  query string.  To  get information from CiteSeer systematically,  we developed a ‘bot’ that  queries  CiteSeer a  publication  list of  each faculty  member and fetches the results. A returned result contains title, publication venue, year of publication, and co-author names. These are stored in our database for  post-processing.  Although CiteSeer does not cover  all  up-to-date  publications,  it  has  more  than  1,100,000 articles in  a  well  formatted  structure.  Moreover,  it  becomes helpful for us to deal with the data that CiteSeer has made much effort to disambiguate the author names. Figure 1. Data processing from CiteSeerCiteSeer was used as  an  augmenter for our dataset,  since some faculty  members  do not  have  the  list  of  their  publications available on  their  web  pages  or  they  have  an  incomplete  list. CiteSeer  helped  in  collecting  more  publication data for  those faculty members.  For example, one of the faculty members had only  4  papers listed  on  his/her webpage,  whereas  we  got  24 papers from CiteSeer.  Sometimes, for the worse, it is difficult to parse the list of published papers when the list is not constantly well  structured while  it  is  relatively easy  to  parse  results  from CiteSeer as it  contains all their publications in the same format. For this reason, we  decided to get publication information from CiteSeer as the only data source for those faculty members.  The number of publications collected from CiteSeer is given in Table 1.2.2.2 Data from Faculty Members HomepagesCollecting  publications  data  from  CiteSeer  was not  enough, because not every faculty member’s publication  was indexed in CiteSeer’s  database.  To  solve  this  problem,  we  crawled the websites of the faculty members and tried to extract publications information  from  it.  Most  of  the faculty  members  follow some pattern in listing their publications. We tracked these patterns and wrote regular expressions  that  capture the publication  data.  For example, a popular pattern is listing authors first followed by a single  dot,  then year of  publication,  after that  the title and  the conference name.   Figure 2. Data processing from Faculty HomepageWe started by downloading the publications page of each faculty member. Then, we used a tool to convert the HTML pages into text files.  This  would  only  keep the text and  eliminate  HTML tags. Then, we developed a program that takes these text files as an input and extracts publications information in accordance with the regular expressions given before. Finally, this program stores the data in the same database.One major challenge was name disambiguation. We developed a simple heuristics to match  the name.  i.e.,  if  the last  name of  a faculty  member  appeared  in  another  faculty  member’s publication list, then it is more likely that this last name belongs to  a  faculty  member  in  the  same  iSchool.  Consequently,  we considered an author as the one whose last name appears on some colleague’s publication  list. To make it easy, when dealing with authors, we assigned an ID for each faculty member. Since each faculty member’s name is listed with  some variation in spelling (sometimes full name and sometimes initials), we defined regular expressions for recognizing their different spellings. These regular expressions would map different spellings to the same ID.In  total,  our  final  dataset  had  1357 publications of  41  faculty members.Table 1. Publications SummaryFrom CiteSeer From  Faculty  Members’ websiteTotal  no.  of  Publications742 615 13572.3 The Need for a Theoretical LensPast  studies  which  used  Social  Network  Analysis  methods  to analyze academic  publication collaboration tend to overlook the influence of  social  identity on clique behavior [7][8][9].  A few studies [10] did use the term social identity, but they did not use it in the context of the theoretical framework put forth by Tajfel and Turner [7].2.3.1 Analysis of Student Project TeamsTo illustrate the drawbacks of those approaches which did not use any theoretical lens to interpret their social network data, we did an  exploratory study  of  team project  reports on  social  network analysis.  Eighteen  team  project  reports,  written by  iSchool graduate students, were obtained from a course instructor with the team names  blinded.  The goal  of  the project  was  to conduct  a social  network  analysis  on  the  collaboration  in  publishing research work among their faculty members. The rationale for selecting these teams is as follows: • All teams  had  almost  the same dataset  of publication collaborations. • All  teams  deployed  computational  methods  with  the help of  social  network analysis software (like Pajek or UCINet) and visualization software (e.g. Netdraw). • Except one team,  all  other student teams did not use any ‘theoretical lens’  Each team was  asked  to  start out with various hypotheses from different  research  questions.  However, there were few  common research  questions  across  multiple  teams.  We  chose  one  such research  question  concerning  ‘inter-disciplinarity’, and  analyzed how  a  team’s  ‘grouping  of  faculty  members  into  arbitrary clusters’ affected  their  findings, and  how  their  idiosyncratic interpretation of  those findings  without a theoretical  lens  led to disparate conclusions. 2.4 Categorization We begin by categorizing faculty members according to their:1. Educational backgrounds2. h-indexes3. Affiliations with Research Centers and LabsBased on the categorization above, we did a sub-network and  a cross-network analysis, and tried to find out if there is any clique behavior [6], and the influence of  social identity on such clique behavior.  For  example,  do  faculty  members  sharing the  same educational background tend to collaborate more with each other than  those  with different backgrounds? What is the influence of h-index [3] on collaboration patterns?  Do faculty members with higher  h-indexes tend not  to  collaborate  much  with  those  with low h-indexes? We use different  centrality  measures  such  as degree centrality, betweeness  centrality  and  closeness centrality to  look  into the influence  of  Social  Identity  on  publication  collaborations.  We would share the results of our findings and analysis in our poster. 3. CONCLUSIONFrom  the  analysis  above,  a  strong  correlation  among group affiliations,  clique  behavior  and  closed collaboration  networks have  been  observed.  When  we tried  to  interpret  the  results  by using  the  ‘Social  Identity  Theory’  lens,  we  found  that  faculty members’ ‘self’ and ‘social’ categorizations - of themselves and of the groups around them - did influence collaboration patterns. Membership within a specific group is a factor of the “zone of  intersection” between  an  individual’s  “personal”  and  “social” identities.  However,  an  individual  would  feel  part  of  the  large community  if  there  is  a  considerable  overlap  between  one’s “personal  and  social  identities”  with  that  of  the  overall community’s  identity.  This  identification  with  the  ‘larger community’, than an exclusive identification with their respective research  centers  and  labs,  would  foster  collaboration  between discrete disciplines, leading to a vibrant inter-disciplinary research climate within iSchools.We conclude that one could thereby avoid idiosyncratic ways of interpreting social  network data by using  a theoretical  lens  like ‘Social Identity Theory’.4. TOOLS USED “Harzing’s Publish or Perish”  was used for computing  the h-indexes of  the  faculty  members.  Pajek,  UCINet and  Netdraw were used for the social network analysis and the visualization of our dataset.5. REFERENCES[1] Cronin,  B.,  & Meho,  L.  2006.  Using  the h-index  to rank influential  information  scientists.  Journal  of  the  American Society  for  Information  Science  and  Technology,  57(9), 1275–1278.[2] Green,  M.  and  M.  E.  Bridget  Warbington. 2004.  The Information  School  Diversity  Appraisal  January  2004 Report, Information School,University of Washington.[3] Hirsch,  J.  E.  2005.  An  index  to  quantify  an  individual's scientific  research  output.  PNAS 102  (46):  16569–16572. doi:10.1073/pnas.0507655102[4] Hogg, M. A., & Vaughan, G. M.  1998.  Social psychology (2nd ed.). Hemel Hempstead: Prentice-Hall[5] Hogg, M. A. 2003. Social identity. In M. R.  Leary & J. P.  Tangney  (Eds.),  Handbook  of  self  and  identity  (pp.  462–479). New York: Guilford. [6] McPherson,  J.  M.,  Smith-Lovin,  L.,  & Cook,  J.  M.  2001. Birds  of  a  feather:  Homophily  in  social  networks.  In  J. Hagan  & K.  S.  Cook (Eds.),  Annual  review of  sociology, vol. 27: 415–444. Palo Alto, CA: Annual Reviews.[7] Tajfel, H. and Turner, J. C. 1986. The social identity theory of  inter-group  behavior.  In  S.  Worchel  and  L.  W.  Austin (eds.),  Psychology  of  Intergroup  Relations.  Chigago: Nelson-Hall.[8] Tajfel, H. 1972. Social categorization. English manuscript of ‘‘La  cate´gorisation  sociale.’’In  S.  Moscovici  (Ed.), Introduction a` la psychologie sociale (Vol. 1, pp. 272–302). Paris:Larousse.[9] Tajfel,  H.,  Billig,  M.,  Bundy,  R.  P.,  & Flament,  C.  1971. Social  categorization  and  intergroup  behaviour.  European Journal of Social Psychology, 1, 149–177.[10] Xu, J., Chau, M. 2006. The social identity of IS: analyzing the collaboration  network  of  the ICIS  conferences  (1980–2005).  Twenty-Seventh  International  Conference  on Information Systems, MilwaukeeAPPENDIXFigure 3. Overall Publication Collaboration NetworkFigure 4. Sub-Network Analysis (Degree Centrality > 2)Figure 5. Cross-Network Analysis (Degree Centrality > 2)

iSchools and Social Identity ??? A Social Network Analysis

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