My paper review

Reading papers is my daily thing. They build my social theory-centric insights and methodological thinking. But I don’t want to read too much into prior work because it might kill my creativity. Thus, I value the paper selection – I appreciate the work which really inspires me. I will regularly update this page to collect my reviews, comments, and random thoughts when I encounter these inspirational papers luckily. Feel free to contact me for any discussions – Random thoughts are not formal academic articles, so be tolerant of the format :)

Major substantive topics:

  • The Sociology of Science, Innovation, Technology, and Organization

Competitive network, spanning structural hole, and innovation. Notes on Thatchenkery, S., & Katila, R. (2021). Seeing what others miss: A competition network lens on product innovation. Organization Science.

Controlling unobserved confounding by converting it into measurable ones. Notes on Brembs, B. (2018). Prestigious science journals struggle to reach even average reliability. Frontiers in Human Neuroscience, 12, 37.

Embeddedness, networks, and economic sociology. Notes on Krippner, G. R., & Alvarez, A. S. (2007). Embeddedness and the intellectual projects of economic sociology. Annual Review of Sociology, 33, 219-240, and Uzzi, B. (1999). Embeddedness in the making of financial capital: How social relations and networks benefit firms seeking financing. American Sociological Review, 481-505.

How to measure innovation? All these papers are following the same idea “moving a larger distance between related content.” Notes on the series of work by Mikko Packalen (Age and the trying out of new ideas, NIH funding and the pursuit of edge science, and Edge factors: Scientific frontier positions of nations), Wu, Lingfei, et al. “Large teams develop and small teams disrupt science and technology.” Nature (2019), Zeng, An, et al. “Fresh teams are associated with original and multidisciplinary research.” Nature Human Behavior (2021), Soda, Giuseppe Beppe, et al. “Networks, creativity, and time: Staying creative through brokerage and network rejuvenation.” Academy of Management Journal (2021), Uzzi, Brian, et al. “Atypical combinations and scientific impact.” Science (2013), and Toubia, Olivier, et al. “How quantifying the shape of stories predicts their success.” Proceedings of the National Academy of Sciences (2021).

A very comprehensive sci-sci study considering all confounding factors. Notes on Park, M., Leahey, E., & Funk, R. (2021). Dynamics of disruption in science and technology. arXiv preprint arXiv:2106.11184.

A complex contagion theoretical model + simulated counterfactuals alongside actual field experiments for agricultural innovation diffusion. Notes on Beaman, Lori, et al. “Can network theory-based targeting increase technology adoption?.” American Economic Review 111.6 (2021): 1918-43.

An interesting quasi-experiment in business venturing. Notes on Cheng, Zhiming, et al. “Childhood adversity and the propensity for entrepreneurship: A quasi-experimental study of the Great Chinese Famine.” Journal of Business Venturing 36.1 (2021): 106063.

Networks in the general sense (human capital and market). Notes on Leonardi, Paul, and Noshir Contractor. “Better people analytics.” Harvard Business Review 96.6 (2018): 70-81, and Jacobs, Abigail Z., and Duncan J. Watts. “A large-scale comparative study of informal social networks in firms.” Management Science (2021).

Normative VS Strategic behaviors from a theory-driven perspective. Notes on Alberto Baccini, and Eugenio Petrovich. “Normative versus strategic accounts of acknowledgment data: The case of the top-five journals of economics.” arXiv:2105.12988v2, and Peng, Tai‐Quan, and Jonathan JH Zhu. “Where you publish matters most: A multilevel analysis of factors affecting citations of internet studies.” Journal of the American Society for Information Science and Technology 63.9 (2012): 1789-1803.

Knowledge module or playing with scientometric statistics. Only big data is not the solution to computational social science problems. Notes on Weis, J.W., Jacobson, J.M. Learning on knowledge graph dynamics provides an early warning of impactful research. Nature Biotechnology (2021), Cassidy R. Sugimoto. Scientific success by numbers. Nature Book Reviews (2021), Watts, Duncan J. “Should social science be more solution-oriented?.” Nature Human Behavior 1.1 (2017): 1-5, and Hilbert, Martin, et al. “Computational communication science: A methodological catalyzer for a maturing discipline.” International Journal of Communication (2019).

Misinformation in the Science of Science. Notes on West, Jevin D., and Carl T. Bergstrom. “Misinformation in and about science.” Proceedings of the National Academy of Sciences 118.15 (2021).

The role of discussion in scientific productivity. Notes on Rose, Michael E., Co-Pierre Georg, and Daniel C. Opolot. “Discussants.” Max Planck Institute for Innovation & Competition Research Paper 20-19 (2020).

A new representation of knowledge: The edges between different fields. Notes on Peng, Hao, et al. “Neural embeddings of scholarly periodicals reveal complex disciplinary organizations.” Science Advances 7.17 (2021): eabb9004.

  • Political Communication, Social Media, Social Networks, and Collective Behaviors

An advantage of observational studies on radical echo chambers over experimental approaches. Notes on Bright, J., Marchal, N., Ganesh, B., & Rudinac, S. (2021). How do individuals in a radical echo chamber react to opposing views? Evidence from a content analysis of Stormfront. Human Communication Research.

Computational communication. Notes on Praet, Stiene, et al. “Comparing automated content analysis methods to distinguish issue communication by political parties on Twitter” and Waldherr, Annie, et al. “Toward a stronger theoretical grounding of computational communication science: How macro frameworks shape our research agendas.” The fourth issue of Computational Communication Research (2021).

A cool experiment with carefully chosen control variables relating to social network structure. Notes on Lu, J.G. (forthcoming). A social network perspective on the Bamboo Ceiling: Ethnic homophily explains why East Asians but not South Asians are underrepresented in leadership in multiethnic environments. Journal of Personality and Social Psychology.

Spurious correlation, comovement, and the consistency in dictionary VS. machine learning-based methods in social media sentiment analysis. Notes on Conrad, F. G., Gagnon-Bartsch, J. A., Ferg, R. A., Schober, M. F., Pasek, J., & Hou, E. (2021). Social media as an alternative to surveys of opinions about the economy. Social Science Computer Review, 39(4), 489-508.

Entropy and the “Surprise” of the public political opinion. Notes on Camargo, C. Q., John, P., Margetts, H. Z., & Hale, S. A. (2021). Measuring the volatility of the political agenda in public opinion and news media. Public Opinion Quarterly.

Don’t blame technology. Notes on The role of (social) media in political polarization: A systematic review, Emily Kubin and Christian von Sikorski, Annals of the International Communication Association. Taylor & Francis, September 2021 and Chen, W., Pacheco, D., Yang, K. C., & Menczer, F. (2021). Neutral bots probe political bias on social media. Nature Communications, 12(1), 1-10.

A very smart idea reforming the division of labor as a graph-coloring game. Notes on Erikson, E., & Shirado, H. (2021). Networks, property, and the division of labor. American Sociological Review86(4), 759-786.

An insightful cross-partisan survey study. Notes on Amsalem, E., Merkley, E., & Loewen, P. J. (2021). Does talking to the other side reduce inter-party hostility? Evidence from three studies. Political Communication, 1-18.

Political communication and toxicity. Notes on Rajadesingan, Ashwin, Ceren Budak, and Paul Resnick. “Political discussion is abundant in non-political subreddits (and less toxic).” arXiv preprint arXiv:2104.09560 (2021), Wojcieszak, Magdalena E., and Diana C. Mutz. “Online groups and political discourse: Do online discussion spaces facilitate exposure to political disagreement?.” Journal of Communication 59.1 (2009): 40-56, and Ventura, T., Munger, K., McCabe, K., & Chang, K.-C., Connective effervescence and streaming chat during political debates. Journal of Quantitative Description: Digital Media, 1 (2021).

How to detect bots on social media? What roles do they play? Notes on González-Bailón, Sandra, and Manlio De Domenico. “Bots are less central than verified accounts during contentious political events.” Proceedings of the National Academy of Sciences 118.11 (2021), and Mesnards, Nicolas Guenon des, et al. “Detecting bots and assessing their impact in social networks.” arXiv preprint arXiv:1810.12398 (2018).

Three similar studies with diverse results on political content spreading on social media through dictionary-based sentiment analysis + regression and limitations of dictionary-based methods. Should we integrate them into a coherent theory? Notes on Alvarez, Raquel, et al. “Sentiment cascades in the 15M movement.” EPJ Data Science 4.1 (2015), Steve Rathje, et al. “Out-group animosity drives engagement on social media.” Proceedings of the National Academy of Sciences Jun 2021, and Brady, William J., et al. “Emotion shapes the diffusion of moralized content in social networks.” Proceedings of the National Academy of Sciences 114.28 (2017): 7313-7318.

Is social network/social media polarized as we thought? Notes on Arvidsson et al., “The Trojan-horse mechanism: How networks reduce gender segregation,” Science Advances (2021), Shore et al., “Network structure and patterns of information diversity on Twitter,” MIS Quarterly (2018), Wu et al., “Cross-partisan discussions on YouTube: Conservatives talk to liberals but liberals don’t talk to conservatives,” AAAI-ICWSM (2021), and Ruggeri et al., “The general fault in our fault lines,” Nature Human Behavior, April 2021.

A very clever and comprehensive experiment design combining naturally occurring experiment, counterfactual, correlation test, and original survey. Notes on ALRABABA’H, A., MARBLE, W., MOUSA, S., & SIEGEL, A. (2021). Can exposure to celebrities reduce prejudice? The effect of Mohamed Salah on Islamophobic behaviors and attitudes. American Political Science Review, 1-18.

Reconsidering observational data and its risks of spurious correlation and endogeneity. Notes on Burton, J.W., Cruz, N. & Hahn, U. Reconsidering evidence of moral contagion in online social networks. Nature Human Behavior (2021).

Sparse information for Tweeter user stance inference. Notes on Samih, Younes, and Kareem Darwish. “A few topical Tweets are enough for effective user stance detection.” Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. 2021.

Different behaviors (retweet, comment, etc.) on social media. Notes on Shugars, S., Gitomer, A., McCabe, S., Gallagher, R. J., Joseph, K., Grinberg, N., Doroshenko, L., Foucault Welles, B., & Lazer, D. (2021). Pandemics, protests, and publics: Demographic activity and engagement on Twitter in 2020. Journal of Quantitative DescriptionDigital Media, 1.

Weak ties. Notes on Mukerjee, Subhayan, Tian Yang, and Sandra González-Bailón. “What counts as a weak tie? A comparison of filtering techniques for weighted networks.” A Comparison of Filtering Techniques for Weighted Networks (April 8, 2019).

  • Quantitative Methods and Data-driven Models

A natural experiment using a modified difference-in-differences (DiD) model. Notes on Yang, Longqi, et al. “The effects of remote work on collaboration among information workers.” Nature Human Behavior (2021): 1-12.

A beautiful statistical analysis of survey data. Notes on D’Acunto, Francesco, Ulrike Malmendier, and Michael Weber. “Gender roles produce divergent economic expectations.” Proceedings of the National Academy of Sciences 118.21 (2021).

A good econometric model with strong explanation power and generalization ability. Notes on Doucouliagos, Chris, and Tom D. Stanley. “Are all economic facts greatly exaggerated? Theory competition and selectivity.” Journal of Economic Surveys 27.2 (2013): 316-339

The power of mobile phone metadata. Notes on Blumenstock, Joshua, Gabriel Cadamuro, and Robert On. “Predicting poverty and wealth from mobile phone metadata.” Science, 2015, Wang, Yuxia, et al. “Migration patterns in China extracted from mobile positioning data.” Habitat International, 2019, and Xu, Jun, et al. “Difference of urban development in China from the perspective of passenger transport around Spring Festival.” Applied Geography, 2017.

An interesting data-driven model studying phase transition in social networks. We should revisit the classic threshold model by Granovetter. Notes on Xie, Jiarong, et al. “Detecting and modeling real percolation and phase transitions of information on social media.” Nature Human Behavior (2021): 1-8.

A good measurement is urgently needed for observational data. We should also focus on identifying the causal effects behind data. Notes on the paper collection in Nature special issue on computational social science, 2021.

  • Health Communication

A semantic network analysis based on crisis communication context. Notes on Zhao, Xinyan, and Hyun Jee Oh. “What fosters inter-organizational frame convergence: Examining a semantic network during the opioid crisis.” Public Relations Review 47.3 (2021): 102042.

Crisis communication and fact-checking. Notes on Zhao, X., Tsang, S. J. (2021). Self-protection by fact-checking: How pandemic information seeking and verifying affect preventive behaviors. Journal of Contingencies and Crisis Management, 1– 14.

Honglin (Carson) Bao
Honglin (Carson) Bao
A student in Organization, Behavior, and Computational Social Science