When Communication Meets Computation: Opportunities, Challenges, and Pitfalls in Computational Communication Science

ABSTRACT The recent increase in digitally available data, tools, and processing power is fostering the use of computational methods to the study of communication. This special issue discusses the validity of using big data in communication science and showcases a number of new methods and applications in the fields of text and network analysis. Computational methods have the potential to greatly enhance the scientific study of communication because they allow us to move towards collaborative large-N studies of actual behavior in its social context. This requires us to develop new skills and infrastructure and meet the challenges of open, valid, reliable, and ethical “big data” research. By bringing together a number of leading scholars in one issue, we contribute to the increasing development and adaptation of computational methods in communication science.

[1]  Philip J. Stone,et al.  Extracting Information. (Book Reviews: The General Inquirer. A Computer Approach to Content Analysis) , 1967 .

[2]  Marshall S. Smith,et al.  The general inquirer: A computer approach to content analysis. , 1967 .

[3]  Russell H. Fazio,et al.  New technologies for the direct and indirect assessment of attitudes. , 1992 .

[4]  M. L. Plume,et al.  SPSS (Statistical Package for the Social Sciences) , 2002, Encyclopedia of Information Systems.

[5]  Peter R. Monge,et al.  Theories of Communication Networks , 2003 .

[6]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[7]  Terrill L. Frantz,et al.  Communication Networks from the Enron Email Corpus “It's Always About the People. Enron is no Different” , 2005, Comput. Math. Organ. Theory.

[8]  Noshir Contractor,et al.  Coevolution of communication and knowledge networks in transactive memory systems: Using computational models for theoretical development , 2006 .

[9]  Vitaly Shmatikov,et al.  Robust De-anonymization of Large Sparse Datasets , 2008, 2008 IEEE Symposium on Security and Privacy (sp 2008).

[10]  W. H. van Atteveldt,et al.  Semantic Network Analysis: Techniques for Extracting, Representing, and Querying Media Content , 2008 .

[11]  A. Pentland,et al.  Life in the network: The coming age of computational social science: Science , 2009 .

[12]  Chong Wang,et al.  Reading Tea Leaves: How Humans Interpret Topic Models , 2009, NIPS.

[13]  Jure Leskovec,et al.  Modeling Information Diffusion in Implicit Networks , 2010, 2010 IEEE International Conference on Data Mining.

[14]  K. Selçuk Candan,et al.  How Does the Data Sampling Strategy Impact the Discovery of Information Diffusion in Social Media? , 2010, ICWSM.

[15]  Loren Collingwood,et al.  Tradeoffs in Accuracy and Efficiency in Supervised Learning Methods , 2012 .

[16]  R. Peng Reproducible Research in Computational Science , 2011, Science.

[17]  Mark Dredze,et al.  You Are What You Tweet: Analyzing Twitter for Public Health , 2011, ICWSM.

[18]  Duncan J. Watts,et al.  Who says what to whom on twitter , 2011, WWW.

[19]  Jeffrey Boase,et al.  No Such Effect? The Implications of Measurement Error in Self-Report Measures of Mobile Communication Use , 2012 .

[20]  Cameron Marlow,et al.  A 61-million-person experiment in social influence and political mobilization , 2012, Nature.

[21]  D. Boyd,et al.  CRITICAL QUESTIONS FOR BIG DATA , 2012 .

[22]  B. Huberman Sociology of science: Big data deserve a bigger audience , 2012, Nature.

[23]  A. Barabasi,et al.  Network science , 2016, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[24]  Yingcai Wu,et al.  Visual Analysis of Topic Competition on Social Media , 2013, IEEE Transactions on Visualization and Computer Graphics.

[25]  T. Graepel,et al.  Private traits and attributes are predictable from digital records of human behavior , 2013, Proceedings of the National Academy of Sciences.

[26]  M. de Rijke,et al.  Automatic Thematic Content Analysis: Finding Frames in News , 2013, SocInfo.

[27]  Malcolm R. Parks Big Data in Communication Research: Its Contents and Discontents , 2014 .

[28]  W. R. Neuman,et al.  The Dynamics of Public Attention: Agenda‐Setting Theory Meets Big Data , 2014 .

[29]  Jignesh M. Patel,et al.  Big data and its technical challenges , 2014, CACM.

[30]  Jeffrey T. Hancock,et al.  Experimental evidence of massive-scale emotional contagion through social networks , 2014, Proceedings of the National Academy of Sciences.

[31]  A. Arvidsson,et al.  Echo Chamber or Public Sphere? Predicting Political Orientation and Measuring Political Homophily in Twitter Using Big Data , 2014 .

[32]  Margaret E. Roberts,et al.  No! Formal Theory, Causal Inference, and Big Data Are Not Contradictory Trends in Political Science , 2014, PS: Political Science & Politics.

[33]  Andreas Jungherr The Logic of Political Coverage on Twitter: Temporal Dynamics and Content , 2014 .

[34]  Jeffrey T. Leek,et al.  Statistics: P values are just the tip of the iceberg , 2015, Nature.

[35]  Matthew Hindman,et al.  Building Better Models , 2015 .

[36]  Stuart Soroka,et al.  Bad News or Mad News? Sentiment Scoring of Negativity, Fear, and Anger in News Content , 2015 .

[37]  William T Riley,et al.  From Big Data to Knowledge in the Social Sciences , 2015, The Annals of the American Academy of Political and Social Science.

[38]  Irene Costera Meijer,et al.  Checking, Sharing, Clicking and Linking: Changing patterns of news use between 2004 and 2014 , 2015 .

[39]  Richard Huskey,et al.  Brain Imaging in Communication Research: A Practical Guide to Understanding and Evaluating fMRI Studies , 2015 .

[40]  Marko A. Hofmann Searching for effects in big data: Why p-values are not advised and what to use instead , 2015, 2015 Winter Simulation Conference (WSC).

[41]  E. Hargittai Is Bigger Always Better? Potential Biases of Big Data Derived from Social Network Sites , 2015 .

[42]  S. Gosling,et al.  Facebook as a research tool for the social sciences: Opportunities, challenges, ethical considerations, and practical guidelines. , 2015, The American psychologist.

[43]  Gary King,et al.  Automating Open Science for Big Data , 2015 .

[44]  Dhavan V. Shah,et al.  Big Data, Digital Media, and Computational Social Science , 2015 .

[45]  Georgios Paltoglou,et al.  Signals of Public Opinion in Online Communication , 2015 .

[46]  Emily B. Falk,et al.  Neural Prediction of Communication-Relevant Outcomes , 2015 .

[47]  Camiel J. Beukeboom,et al.  Blinded by the Light: How a Focus on Statistical “Significance” May Cause p-Value Misreporting and an Excess of p-Values Just Below .05 in Communication Science , 2015 .

[48]  Tsutomu Suzuki,et al.  Emerging From the Cocoon? Revisiting the Tele-Cocooning Hypothesis in the Smartphone Era , 2015, J. Comput. Mediat. Commun..

[49]  Weihua An,et al.  Fitting ERGMs on big networks. , 2016, Social science research.

[50]  Taha Yasseri,et al.  P-Values: Misunderstood and Misused , 2016, Front. Phys..

[51]  Albert-Lszl Barabsi,et al.  Network Science , 2016, Encyclopedia of Big Data.

[52]  R. Michael Alvarez,et al.  Computational Social Science: Discovery and Prediction , 2016, Analytical Methods for Social Research.

[53]  Martin Bouchard,et al.  Liking and hyperlinking: Community detection in online child sexual exploitation networks. , 2016, Social science research.

[54]  Damian Trilling,et al.  Taking Stock of the Toolkit , 2016, Rethinking Research Methods in an Age of Digital Journalism.

[55]  Richard Bonneau,et al.  Big Data, Social Media, and Protest: Foundations for a Research Agenda , 2016, Computational Social Science.

[56]  Carina Jacobi,et al.  Quantitative analysis of large amounts of journalistic texts using topic modelling , 2016, Rethinking Research Methods in an Age of Digital Journalism.

[57]  Hanna Wallach Computational Social Science: Toward a Collaborative Future , 2016 .

[58]  N. Lazar,et al.  The ASA Statement on p-Values: Context, Process, and Purpose , 2016 .

[59]  Justin Grimmer,et al.  Measuring Representational Style in the House: The Tea Party, Obama, and Legislators' Changing Expressed Priorities , 2016, Computational Social Science.

[60]  Marshall Scott Poole,et al.  An illustration of the relational event model to analyze group interaction processes , 2016 .

[61]  Cristian Danescu-Niculescu-Mizil,et al.  Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussions , 2016, WWW.

[62]  Daniel Tumminelli O'Brien,et al.  Using small data to interpret big data: 311 reports as individual contributions to informal social control in urban neighborhoods. , 2016, Social science research.

[63]  Peter Neijens,et al.  How Much Time Do You Spend Online? Understanding and Improving the Accuracy of Self-Reported Measures of Internet Use , 2017 .

[64]  Michael Scharkow,et al.  How Measurement Error in Content Analysis and Self-Reported Media Use Leads to Minimal Media Effect Findings in Linkage Analyses: A Simulation Study , 2017 .

[65]  Wouter van Atteveldt,et al.  Clause Analysis: Using Syntactic Information to Automatically Extract Source, Subject, and Predicate from Texts with an Application to the 2008–2009 Gaza War , 2017, Political Analysis.

[66]  G. King,et al.  How the news media activate public expression and influence national agendas , 2017, Science.

[67]  Mark E. J. Newman,et al.  Network structure from rich but noisy data , 2017, Nature Physics.

[68]  Damian Trilling Big Data, Analysis of , 2017 .

[69]  Michael Sedlmair,et al.  More than Bags of Words: Sentiment Analysis with Word Embeddings , 2018 .

[70]  Silke Adam,et al.  Applying LDA Topic Modeling in Communication Research: Toward a Valid and Reliable Methodology , 2018 .

[71]  Balázs Bodó,et al.  Tackling the Algorithmic Control Crisis – the Technical, Legal, and Ethical Challenges of Research into Algorithmic Agents , 2018 .

[72]  Gediminas Adomavicius,et al.  Mind the Gap: Accounting for Measurement Error and Misclassification in Variables Generated via Data Mining , 2017, Inf. Syst. Res..

[73]  Jeff Gill,et al.  Comments from the New Editor , 2018, Political Analysis.

[74]  B. Nyhan,et al.  Selective exposure to misinformation: Evidence from the consumption of fake news during the 2016 U.S. presidential campaign , 2018 .

[75]  Dorothy R. Carter,et al.  How Team Interlock Ecosystems Shape the Assembly of Scientific Teams: A Hypergraph Approach , 2018, Communication methods and measures.

[76]  M. S. Weber,et al.  Methods and Approaches to Using Web Archives in Computational Communication Research , 2018 .

[77]  E-Step Structural Topic Models for Open Ended Survey Responses , 2022 .