Relevance & Research Question: Online intermediaries such as social network sites (e.g., Facebook) or search engines (e.g., Google) are playing an increasingly important role in citizens' information diets. With their algorithmically and socially driven recommender systems, these platforms are assumed to cater to the predispositions of users who are - by and large - not primarily interested in news and politics. Yet recent research indicates that intermediaries also foster incidental, i.e., non-intentional exposure to news. We therefore ask: do online intermediaries indeed drive away citizens from news? Or do they actually foster - non-political and political - news exposure? And what is the role of personal characteristics such as education and political interest?

Methods & Data: We recruited 7,775 study participants from online access panels with a continuous web tracking in six countries: France, Germany, Italy, Spain, UK and US. We combine observed data on web browsing behavior for 3 months with the complementary advantages of surveys of the same set of participants. A machine learning model trained on the crawled text of newspaper articles is used to automatically identify political news articles.

Results: The results from random-effects within-between models that separate daily variation from stable behavior show that across countries and personal characteristics, using online intermediaries increases the number of newspaper articles and sources of news consumption. These effects are stable across personal characteristics and countries as well as political and non-political news.

Added Value: The big online platforms counteract societal fragmentation tendencies and news avoidance. As such, the findings have implications for scholarly and popular debates on the dangers to democracy posed by digital high-choice media environments.

Relevance & Research Question: We examine the prevalence and origins of populist alternative news use and the relationship with voting for populist parties in Germany. Empirical evidence of exposure to populist alternative news use in Germany is scarce and is mostly based on inaccurate self-reported survey data.

Methods & Data: We draw from two combined data sets of web-tracking and survey data which were collected during the 2017 German Bundestag campaign (1,523 participants) and the 2019 European Parliamentary election campaign in Germany (1,009 participants). We investigate the relationships between exposure to populist alternative news and political preferences using two-component count data regression modeling.

Results: Results indicate that while populist alternative news draw more interest during first-order election campaigns (Bundestagswahl), they do not reach large user groups. Moreover, most users visit their websites rather seldom. Nonetheless, our data suggest alternative news exposure is strongly linked to voting for populist parties. Our data also shed light on the role of platforms in referring users to populist alternative news. About 40% of website visits originated from Facebook alone in both data sets, another third of visits from search engines.

Added Value: We provide novel insights into the prevalence and origins of populist alternative news use in Germany using fine-granular web tracking data. The large share of populist alternative news use originating from social media platforms fuels debates of algorithmic accountability.

Relevance & Research Question:

Political polls are at the same time questionable and irreplaceable. Elections after elections they show their limits but no other method has yet been proven to be more accurate or reliable.

In this paper, we would like to present the experiment we are conducting about the 2021 german federal elections whose objective is trying to improve opinion monitoring thanks to web navigation data. More precisely, our goal is to enrich insights and improve predictions about voting intention thanks to a combination of survey results and news consumption on the internet.

Methods & Data:

For now more than 5 years, respondi has been involved in combining survey data with passive data. In Germany we operate a nat rep panel (sample size obviously changes every month, as we have to deal with churn, cleansing operation, but we keep it close to n=2500 over time. Panel size is currently n=2541) whose members have accepted to equip (at least) one connected device of theirs with a software which monitors (among other things) which website they visit.

The design of the experiment is the following: we survey these people about their voting intention every week (we plan to conduct 8 waves of interrogation, each of them collecting around 350 completes), and in the mean time we collect all the news (related to the elections or not) they read online (based on our previous observations, we collect around 30k articles per month in Germany).

News articles are classified and summarized using a deep learning language model based on Google’s BERT and fine-tuned for topic detection. We will thus be able to identify patterns of news consumption which are associated with changes in opinion.

Results:

Displayed on a live dashboard powered by Tableau.

Obviously no results are available yet. Our intention is to associate change in opinion with the content read : which message did trigger a change in opinion for which audience ?

Added Value:

Ultimately, if this experiment works, it leads to a new type of election monitoring: real time measurement of change in opinion, and granular explanations of the changes.