Meysam Alizadeh

Living in the Post API age, researchers face unprecedented challenges in obtaining social media data, while users are concerned about how big tech companies use their data. Data donation offers a promising alternative, however, its scalability is limited by low participation and high dropout rates. Research suggests that data marketplaces could be a solution, but its realization remains challenging due to theoretical gaps in treating data as an asset. This paper examines whether data marketplaces can increase individuals willingness to sell their X (Twitter) data package and the minimum price they would accept. It also explores how privacy protections and the type of data buyer may affect these decisions. Results from two preregistered online survey experiments show that a data marketplace increases participants' willingness to sell their X data by 12 to 25 percentage points compared to data donation (depending on treatments), and by 6.8 points compared to onetime purchase offers. Although difference in minimum acceptable prices are not statistically significant, over 64 percentage of participants set their price within the marketplace's suggested range (0.25 to 2), substantially lower than the amounts offered in prior onetime purchase studies. Finally, in the marketplace setting, neither the type of buyer nor the inclusion of a privacy safeguard significantly influenced participants willingness to sell.

Academics increasingly acknowledge the predictive power of social media for a wide variety of events and, more specifically, for financial markets. Anecdotal and empirical findings show that cryptocurrencies are among the financial assets that have been affected by news and influencers' activities on Twitter. However, the extent to which Twitter crypto influencer's posts about trading signals and their effect on market prices is mostly unexplored. In this paper, we use LLMs to uncover buy and not-buy signals from influencers and news outlets' Twitter posts and use a VAR analysis with Granger Causality tests and cross-correlation analysis to understand how these trading signals are temporally correlated with the top nine major cryptocurrencies' prices. Overall, the results show a mixed pattern across cryptocurrencies and temporal periods. However, we found that for the top three cryptocurrencies with the highest presence within news and influencer posts, their aggregated LLM-detected trading signal over the preceding 24 hours granger-causes fluctuations in their market prices, exhibiting a lag of at least 6 hours. In addition, the results reveal fundamental differences in how influencers and news outlets cover cryptocurrencies.

Twitter data has been widely used by researchers across various social and computer science disciplines. A common aim when working with Twitter data is the construction of a random sample of users from a given country. However, while several methods have been proposed in the literature, their comparative performance is mostly unexplored. In this paper, we implement four common methods to create a random sample of Twitter users in the US: 1% Stream, Bounding Box, Location Query, and Language Query. Then, we compare these methods according to their tweet- and user-level metrics as well as their accuracy in estimating the US population. Our results show that users collected by the 1% Stream method tend to have more tweets, tweets per day, followers, and friends, a fewer number of likes, are younger accounts, and include more male users compared to the other three methods. Moreover, it achieves the minimum error in estimating the US population. However, the 1% Stream method is time-consuming, cannot be used for the past time frames, and is not suitable when user engagement is part of the study. In situation where these three drawbacks are important, our results support the Bounding Box method as the second-best method.

Twitter data has been widely used by researchers across various social and computer science disciplines. A common aim when working with Twitter data is the construction of a random sample of users from a given country. However, while several methods have been proposed in the literature, their comparative performance is mostly unexplored. In this paper, we implement four common methods to collect a random sample of Twitter users in the US: 1% Stream, Bounding Box, Location Query, and Language Query. Then, we compare the methods according to their tweet- and user-level metrics as well as their accuracy in estimating US population with and without using inclusion probabilities of various demographics. Our results show that the 1% Stream method performs differently than others in tweet- and user-level metrics, and best for the construction of a population representative sample. We discuss the conditions under which the 1% Stream method may not be suitable and suggest the Bounding Box method as the second-best method to use.

Some major social media companies are announcing plans to tokenize user engagements, derived from blockchain-based decentralized social media. This would bring financial and reputational incentives for engagement, which might lead users to post more objectionable content. Previous research showed that financial or reputational incentives for accuracy decrease the willingness to share misinformation. However, it is unclear to what extent such outcome would change if engagements instead of accuracy were incentivized, which is a more realistic scenario. To address this question, we conducted a survey experiment to examine the effects of hypothetical token incentives. We find that a simple nudge about the possibility of earning token-based points for the achieved user engagements increases the willingness to share different kinds of news, including misinformation. The presence of penalties for objectionable posts diminishes the positive effect of tokenization rewards on misinformation sharing, but it does not eliminate it. These results have policy implications for content moderation practices if platforms embrace decentralization and engagement tokenization.

Many NLP applications require manual text annotations for a variety of tasks, notably to train classifiers or evaluate the performance of unsupervised models. Depending on the size and degree of complexity, the tasks may be conducted by crowd workers on platforms such as MTurk as well as trained annotators, such as research assistants. Using four samples of tweets and news articles (n= 6,183), we show that ChatGPT outperforms crowd workers for several annotation tasks, including relevance, stance, topics, and frame detection. Across the four datasets, the zero-shot accuracy of ChatGPT exceeds that of crowd workers by about 25 percentage points on average, while ChatGPT’s intercoder agreement exceeds that of both crowd workers and trained annotators for all tasks. Moreover, the per-annotation cost of ChatGPT is less than $0.003—about thirty times cheaper than MTurk. These results demonstrate the potential of large language models to drastically increase the efficiency of text classification.