The negative environmental and economic impacts of many invasive species are well known. However, given the increased homogenization of global biota, and the difficulty of eradicating species once established, a balanced approach to considering the impacts of invasive species is needed. The marbled crayfish (Procambarus virginalis) is a parthenogenetic freshwater crayfish that was first observed in Madagascar around 2005 and has spread rapidly. We present the results of a socio-economic survey (n = 385) in three regions of Madagascar that vary in terms of when the marbled crayfish first arrived. Respondents generally considered marbled crayfish to have a negative impact on rice agriculture and fishing, however the animals were seen as making a positive contribution to household economy and food security. Regression modeling showed that respondents in regions with longer experience of marbled crayfish have more positive perceptions. Unsurprisingly, considering the perception that crayfish negatively impact rice agriculture, those not involved in crayfish harvesting and trading had more negative views towards the crayfish than those involved in crayfish-related activities. Food preference ranking and market surveys revealed the acceptance of marbled crayfish as a cheap source of animal protein; a clear positive in a country with widespread malnutrition. While data on biodiversity impacts of the marbled crayfish invasion in Madagascar are still completely lacking, this study provides insight into the socio-economic impacts of the dramatic spread of this unique invasive species. "Biby kely tsy fantampiaviana, mahavelona fianakaviana" (a small animal coming from who knows where which supports the needs of the family). Government worker Analamanga, Madagascar. OPEN ACCESS Citation: Andriantsoa R, Jones JPG, Achimescu V, Randrianarison H, Raselimanana M, Andriatsitohaina M, et al. (2020) Perceived socioeconomic impacts of the marbled crayfish invasion in Madagascar. PLoS ONE 15(4): e0231773.

Global crises provide a fertile environment for the proliferation of disinformation, rumors, and conspiracy narratives. We investigate people's perceptions and beliefs related to COVID-19 in Romania, during the lockdown period (April 2020) and during the state of alert period (July 2020), by fielding two surveys with different modes of collection (CATI and web). Building on measures tested in other countries, we identify the public’s vulnerability to conspiracy narratives and its willingness to comply with public health guidance. Using Structural Equation Modeling, we check if individuals exhibiting pro-Russian or anti-Western attitudes believe more strongly in COVID-19 conspiracy narratives compared to the rest of the population. Then, we check if those believing conspiracy narratives are less susceptible to comply with public health recommendations.We find in both surveys that holding conspiracy beliefs is a mediator between distrusting Western actors and noncompliance with COVID-19 guidelines. Thus, pro-Russian and anti-EU, U.S. and NATO attitudes are linked to stronger conspiracy beliefs, which relate to lower levels of concern and knowledge regarding the virus, which in turn can reduce compliance with guidelines. This suggests that openness to anti-Western narratives may have behavioral consequences. These findings highlight the potential sources of unsafe behaviors during the pandemic and can inform official communication strategies meant to counter both disinformation and non-compliance with public health policies.

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The authenticity of public debate is challenged by the emergence of networks of non-genuine users (such as political bots and trolls) employed and maintained by governments to influence public opinion. To tackle this issue, researchers have developed algorithms to automatically detect non-genuine users, but it is not clear how to identify relevant content, what features to use and how often to retrain classifiers. Users of online discussion boards who informally flag other users by calling them out as paid trolls provide potential labels of perceived propaganda in real time. Against this background, we test the performance of supervised machine learning models (regularized regression and random forests) to predict discussion board comments perceived as propaganda by users of a major Romanian online newspaper. Results show that precision and recall are relatively high and stable, and re-training the model on new labels does not improve prediction diagnostics. Overall, metadata (particularly a low comment rating) are more predictive of perceived propaganda than textual features. The method can be extended to monitor suspicious activity in other online environments, but the results should not be interpreted as detecting actual propaganda.

The truth value of any new piece of information is not only investigated by media platforms, but also debated intensely on internet forums. Forum users are fighting back against misinformation, by informally flagging suspicious posts as false or misleading in their comments. We propose extracting posts informally flagged by Reddit users as a means to narrow down the list of potential instances of disinformation. To identify these flags, we built a dictionary enhanced with part of speech tags and dependency parsing to filter out specific phrases. Our rule-based approach performs similarly to machine learning models, but offers more transparency and interactivity. Posts matched by our technique are presented in a publicly accessible, daily updated, and customizable dashboard. This paper offers a descriptive analysis of which topics, venues, and time periods were linked to perceived misinformation in the first half of 2020, and compares user flagged sources with an external dataset of unreliable news websites. Using this method can help researchers understand how truth and falsehood are perceived in the subreddit communities, and to identify new false narratives before they spread through the larger population.