Reacción a "Study shows X's (Twitter) algorithm moves users towards more conservative political positions"

Is the research of good quality?

"It is an experimental study with a sample of nearly 5,000 X users in the United States, conducted over a seven-week period in the summer of 2023. This period corresponds to six months after Elon Musk's purchase of Twitter and one year before he publicly endorsed the then-Republican candidate for the US presidency, Donald Trump. Both in terms of sample size and data collection and analysis procedures, this is rigorous research, as expected from a scientific journal of the scientific quality of Nature. It should also be noted that the research team conducted the experiment on its own and without the collaboration of X, which reinforces the independence of the results."

How does it fit with existing evidence?

"Since networks such as Facebook and Twitter appeared in the first decade of the 2000s, theories such as the echo chamber and the filter bubble have suggested that social networks act as selective filters for certain opinions. According to these theories, through their secret algorithms, networks are acting as gatekeepers of information, increasing the visibility of certain content and reducing others. In their quest to maximize user loyalty, social networks have become inadvertent filters of the information their users consume.

Over the last two decades, several studies have been conducted to measure these filtering and selective content reinforcement effects by social networks as a whole. What is unique about this study published in Nature is that it analyzes the effects of X on the political positioning of its users, analyzing respectively the “For You” mode of X, which presents posts according to an algorithmic selection determined by the social network itself, and the “Following” mode, where each user decides which accounts to view and receives posts in chronological order. The study has found that, when used repeatedly, the “For You” mode, the algorithmic selection, encourages X users to shift toward more conservative political positions.

However, the effect does not affect everyone in the same way, but varies according to their starting position on the ideological spectrum. Those who at the beginning of the experiment defined themselves as progressive (“liberal,” according to the study's terminology) experienced a relatively limited shift toward conservative positions; on the other hand, those who initially claimed to align with conservative or independent positions evolved toward even more conservative positions".

The data is on US users. Could it be that in other countries the algorithm also steers users toward more conservative views?

"It could be. However, at the moment it is only a hypothesis. Further studies would be needed to verify whether the specific configuration of X in each country and in each language translates into a similar phenomenon of ideological shift toward conservative positions as that detected in the United States. It should also be borne in mind that, beyond X, the political particularities of each country may contribute to enhancing or, conversely, moderating this effect. For now, what this study allows us to affirm is that this effect has been verified in the United States."

Are there any important limitations to consider?

"The authors of the study highlight two main limitations: first, the fact that the results must be limited to the X network and, second, that the time period in which the experiment was conducted has a significant impact.

Regarding the first limitation, we should not exaggerate by saying that all social networks influence the political opinions of their users in the same way as X. In fact, the authors of the study point out that the preferences of the owners of each platform may cause each social network to influence its users differently.

As for the time limitation, the study was conducted over a period of seven weeks in the summer of 2023. Although this is a relatively long period of time, it does not allow us to determine the effects over longer periods, such as years of exposure to social media".