Strategies for Social Tie Selections in Political Contexts: Homophily-Related Strategy Specification and Drivers of Bias for Liberals and Conservatives
Computational Social Science
Large-scale political polarization is driven by individual choices to associate with similar others (“homophily”). Despite political homophily being studied generally, no work has been done to understand the exact homophily-related selection strategies individuals utilize for social ties in political contexts. In this paper, we construct an experimental paradigm using emotional reactions in response to pictures of police brutality in the United States to explicate the selection strategies employed by liberals and conservatives. We find that both liberals and conservatives utilize a tie selection strategy we term “biased selection” wherein they aim largely to maximize homophily (i.e. choose the most similar others) but may, at times, forego more similar others in favor of choosing members of their political party who express more extreme emotional responses (very emotional, for liberals; unemotional, for conservatives). We find no significant difference between conservatives and liberals’ bias to select more emotionally extreme members of their political party, and both conservatives and liberals seem to be motivated to this bias by the desire to associate with emotionally dissimilar (and cognitively similar, for liberals) others. Lastly, we find that political heterogeneity in conservative participants’ families lowers their bias towards emotionally extreme (unemotional) members of their party.
Understanding and Characterizing Large-Scale Social Movements with Respect to Collective and Individual Emotions
Computational Social Science
The formation, development, and outcome of social movements are shaped by individuals’ emotions. However, despite the theoretical importance of people’s emotions about social movements, the complexities and evolving nature of these emotions are not thoroughly understood. In particular, social scientists lack empirical evidence over what such emotions look like and how they change over time. Furthermore: to the extent that they do have results, they are largely based on in-lab studies which are, at best, approximations of the real world.
With the rise of social media and, in particular, the new expression of social movements on social media platforms, we are able to access large amounts of data that can give us insight into human behavior as it relates to this topic. Further, with increased technological capabilities around sentiment analysis, we are able to analyze and draw conclusions from this large set of data. In this project, we use sentiment analysis as well as computational and statistical methods to measure and analyze people’s emotional stances on social movements based on public tweets on the social media site Twitter.
Fitting and Interpreting Predictive Models to Identify Gender Bias Over Time in Reddit Comments
Machine Learning
Beaten by only Facebook, Google, and Youtube, www.reddit.com gets the fourth most traffic to a website in the world. It is known to some as the “web’s most influential community”. The CEO of Reddit boasts the website as “a place for open and honest conversations– ‘open and honest meaning authentic, meaning messy, meaning the best and worst and realest and weirdest parts of humanity”.
And in all that authenticity: Daily Dot wrote that asking whether Reddit is sexist “hardly seems worth an argument” because the answer is so obvious: “it is”. A question on reddit posted in 2012:“Why is reddit so anti-women?”generated over 1,700 replies. A subreddit thread, r/TwoXChromosomes, has opened up for women to complain about misogyny on the website.
As such an influential platform, it is important for Reddit to fully understand the legitimacy of this claim. In this paper, I use machine learning to analyze comments from 2006 - 2010 with the ultimate goal of understanding talk along gender boundaries in reddit comments, as well as how this speech has changed over time.
Creation of Plasmodium falciparum Hsp90 Selective Inhibitors for Antimalarial Drug Development
Computational Biochemistry
Malaria remains one of the largest public health challenges to this day with an estimated 3.3 billion people at risk of infection, causing 584,000 deaths in 2014 alone. The Plasmodium falciparum parasite causes the most dangerous form of malaria and has developed resistance to almost all current antimalarial drugs. This study aims to provide a solid basis for developing new drugs to treat malaria. We do this by targeting the chaperone protein P.falciparum heat shock protein 90 (PfHsp90) which is responsible for the proper folding of multiple integral proteins in the organism and is essential to the erythrocytic life cycle of the parasite; inhibition of PfHsp90 effectively impedes parasite development. Geldanamycin is a naturally occurring, potent inhibitor of Hsp90, but is not selective enough for PfHsp90 over human host Hsp90 (HsHsp90), impeding its potential as a therapeutic. We designed and created 45 structural analogs of geldanamycin to increase selectivity. Based on computational testing, a number of promising candidates are identified for drug testing; 19 of the created inhibitors are predicted to inhibit PfHsp90 with greater selectivity, enabling possible future therapeutic use. The quinone moiety of geldanamycin was also found to be integral for selective binding to PfHsp90. In conclusion, we have identified a mechanism of selectivity which can help in the creation of other potential inhibitors and proposed selective, effective inhibitors for use in the synthesis of new drugs to combat this dangerous disease.