Storia: Summarizing Social Media Content based on Narrative Theory using Crowdsourcing

People from all over the world use social media to share thoughts and opinions about events, and understanding what people say through these channels has been of increasing interest to researchers, journalists, and marketers alike. However, while automatically generated summaries enable people to consume large amounts of data efficiently, they do not provide the context needed for a viewer to fully understand an event. Narrative structure can provide templates for the order and manner in which this data is presented to create stories that are oriented around narrative elements rather than summaries made up of facts. In this paper, we use narrative theory as a framework for identifying the links between social media content. To do this, we designed crowdsourcing tasks to generate summaries of events based on commonly used narrative templates. In a controlled study, for certain types of events, people were more emotionally engaged with stories created with narrative structure and were also more likely to recommend them to others compared to summaries created without narrative structure

ChatGPT and the AI Act

It is not easy being a tech regulator these days. The European institutions are working hard towards finalising the AI Act in autumn, and then generative AI systems like ChatGPT come along! In this essay, we comment the European AI Act by arguing that its current risk-based approach is too limited for facing ChatGPT & co

The European AI Act and How It Matters for Research into AI in Media and Journalism

The protection of fundamental rights, and the human-centric, ethical and responsible use of Artificial Intelligence (AI) technologies in general is a central ambition of the European AI strategy, with no lesser goal than “spearhead[ing] the development of new ambitious global norms” (European Commission 2021). Like the General Data Protection Regulation before it, the draft AI Act can be expected to set a new tone for the debate around ‘responsible AI’ both within and beyond Europe. It is one of the first attempts worldwide to cut through an increasingly opaque jungle of private and public ethical guidelines in order to formulate binding regulatory standards for what exactly responsible and human-centric AI must mean.The draft AI Act is relevant not only to potential producers and users of AI, but also to a growing community of scholars that is interested in the normative implications of AI and wants to find ways to make the notion of ‘responsible use’ of AI meaningful. Scholars have an important role to play in informing the emerging policies around AI with their insights, as well as studying the consequences once policies are adopted. As such, the primary goal of this commentary is to explore the relevancy of the draft AI Act for media and journalism, as well as to stimulate the community of media scholars to engage further with the potential implications of the regulation

My Future with My Chatbot:A Scenario-Driven, User-Centric Approach to Anticipating AI Impacts

As a general purpose technology without a concrete pre-defined purpose, personal chatbots can be used for a whole range of objectives, depending on the personal needs, contexts, and tasks of an individual, and so potentially impact a variety of values, people, and social contexts. Traditional methods of risk assessment are confronted with several challenges: the lack of a clearly defined technology purpose, the lack of clearly defined values to orient on, the heterogeneity of uses, and the difficulty of actively engaging citizens themselves in anticipating impacts from the perspective of their individual lived realities. In this article, we leverage scenario writing at scale as a method for anticipating AI impact that is responsive to these challenges. The advantages of the scenario method are its ability to engage individual users and stimulate them to consider how chatbots are likely to affect their reality and so collect different impact scenarios depending on the cultural and societal embedding of a heterogeneous citizenship. Empirically, we tasked 106 US-based participants to write short fictional stories about the future impact (whether desirable or undesirable) of AI-based personal chatbots on individuals and society and, in addition, ask respondents to explain why these impacts are important and how they relate to their values. In the analysis process, we map those impacts and analyze them in relation to socio-demographic as well as AI-related attitudes of the scenario writers. We show that our method is effective in (1) identifying and mapping desirable and undesirable impacts of AI-based personal chatbots, (2) setting these impacts in relation to values that are important for individuals, and (3) detecting socio-demographic and AI-attitude related differences of impact anticipation.</p

Beyond mystery: Putting algorithmic accountability in context

Critical algorithm scholarship has demonstrated the difficulties of attributing accountability for the actions and effects of algorithmic systems. In this commentary, we argue that we cannot stop at denouncing the lack of accountability for algorithms and their effects but must engage the broader systems and distributed agencies that algorithmic systems exist within; including standards, regulations, technologies, and social relations. To this end, we explore accountability in “the Generated Detective,” an algorithmically generated comic. Taking up the mantle of detectives ourselves, we investigate accountability in relation to this piece of experimental fiction. We problematize efforts to effect accountability through transparency by undertaking a simple operation: asking for permission to re-publish a set of the algorithmically selected and modified words and images which make the frames of the comic. Recounting this process, we demonstrate slippage between the “complication” of the algorithm and the obscurity of the legal and institutional structures in which it exists

LST1 promotes the assembly of a molecular machinery responsible for tunneling nanotube formation

Carefully orchestrated intercellular communication is an essential prerequisite for the development of multicellular organisms. In recent years, tunneling nanotubes (TNT) have emerged as a novel and widespread mechanism of cell-cell communication. However, the molecular basis of their formation is still poorly understood. In the present study we report that the transmembrane MHC class III protein LST1 induces the formation of functional nanotubes and is required for endogenous nanotube generation. Mechanistically, we found LST1 to induce nanotube formation by recruiting the small GTPase RalA to the plasma membrane and promoting its interaction with the exocyst complex. Furthermore, we determined LST1 to recruit the actin-crosslinking protein filamin to the plasma membrane and to interact with M-Sec, myosin and myoferlin. These results allow us to suggest a molecular model for nanotube generation. In this proposal LST1 functions as a membrane scaffold mediating the assembly of a multimolecular complex, which controls the formation of functional nanotubes

SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods

In the last few years thousands of scientific papers have investigated sentiment analysis, several startups that measure opinions on real data have emerged and a number of innovative products related to this theme have been developed. There are multiple methods for measuring sentiments, including lexical-based and supervised machine learning methods. Despite the vast interest on the theme and wide popularity of some methods, it is unclear which one is better for identifying the polarity (i.e., positive or negative) of a message. Accordingly, there is a strong need to conduct a thorough apple-to-apple comparison of sentiment analysis methods, \textit{as they are used in practice}, across multiple datasets originated from different data sources. Such a comparison is key for understanding the potential limitations, advantages, and disadvantages of popular methods. This article aims at filling this gap by presenting a benchmark comparison of twenty-four popular sentiment analysis methods (which we call the state-of-the-practice methods). Our evaluation is based on a benchmark of eighteen labeled datasets, covering messages posted on social networks, movie and product reviews, as well as opinions and comments in news articles. Our results highlight the extent to which the prediction performance of these methods varies considerably across datasets. Aiming at boosting the development of this research area, we open the methods' codes and datasets used in this article, deploying them in a benchmark system, which provides an open API for accessing and comparing sentence-level sentiment analysis methods

Leveraging Professional Ethics for Responsible AI

Applying AI techniques to journalism