26 Questões de Usos de linguagem em meio digital: “internetês”

At dusk, neighbours open their phones to the Maplewood Community Chat. Messages flood in about a weekend cleanup, a local history talk and safety reminders. Elena posts a link to the town hall’s official page. Jamal replies with a forwarded note from an unknown source. Others share photos of past events. Some residents ignore the chat and wait for printed leaflets pinned to the notice board. By week’s end, attendance depends on how people interpret and trust the digital conversation. Identify which action demonstrates a critical use of the community chat to support informed participation among residents.

1. A) Relying on group chat volume as proof of fact
2. B) Posting images of events without context
3. C) Verifying information before sharing messages
4. D) Forwarding messages received without checking sources
5. E) Ignoring chat notifications and using printed leaflets

Community members in CityConnect, a digital platform, receive personalized article recommendations and discussion prompts based on their past interactions. A recent survey found that 68% of users mostly interacted with like-minded peers. While 74% reported feeling valued and engaged, several long-term members noted that they rarely encountered contrasting opinions. The platform’s AI filters, designed to boost engagement metrics, curate content that aligns with users’ existing views, subtly shaping their online social circles and discussion topics. Analise the text and identify which conclusion best reflects the platform’s impact on users’ social interactions?

1. A) It fosters meaningful conversations among users with varied opinions.
2. B) It prioritizes content engagement metrics over quality of interaction.
3. C) It increases productivity by organizing information efficiently.
4. D) It limits exposure to differing viewpoints, reinforcing existing beliefs.
5. E) It encourages users to connect with diverse groups beyond their interests.

Why do we often see only stories that reinforce our own views on social media? Many platforms use personalized algorithms that analyze our clicks, likes, and comments to select content for our feeds. A recent study found that 75% of news items shown to users match their existing opinions, limiting exposure to different viewpoints. This digital filter bubble can affect how individuals form opinions and interact with their communities, reinforcing biases and reducing critical engagement. To become more informed digital citizens, people must identify ways to break this cycle and foster a more balanced online information environment. Identify which initiative best breaks the filter bubble and promotes diversity of opinions on social media?

1. A) Design a peer-review system where friends vote on the accuracy of shared posts
2. B) Encourage users to reduce screen time to limit exposure to biased content
3. C) Create closed groups of like-minded individuals for focused discussions
4. D) Implement mandatory fact-check labels on all news articles in the feed
5. E) Develop an algorithm that intentionally introduces diverse perspectives into users’ feeds

During lunch break, a group of friends sits in the courtyard, smartphones in hand, composing messages for their class chat. They rely on predictive text suggestions to finish sentences quickly. Over time, they notice their posts include identical phrases and lose personal tone. While they appreciate the speed, their teacher cautions that heavy dependence on this tool may limit originality and authentic self-expression in digital communication. Analise the text and identify which impact of predictive text technology on students’ social practices is highlighted?

1. A) Strengthening students’ spelling accuracy over time
2. B) Encouraging deeper reflection before posting online
3. C) Facilitating uniform language patterns among students
4. D) Improving face-to-face communication skills during breaks
5. E) Expanding creative vocabulary use in digital messages

Excerpt from a user report: “I regularly browse a news app that employs a recommendation algorithm to present stories based on my past clicks. Over time, I realize I see more articles that reflect my own views and very few that challenge them. Although I discover interesting content faster, I feel my exposure to new perspectives has narrowed significantly.” Compare the algorithmic curation described in the text with the following scenarios: which analogy best illustrates its effect on users’ information exposure?

1. A) A newsstand that displays newspapers from different countries equally to its customers
2. B) A map that highlights all roads, not just the main highway, to guide travelers
3. C) A museum that rotates exhibits equally from each historical period for its visitors
4. D) A bookshelf that organizes all titles alphabetically, ensuring equal access to every author
5. E) A radio tuner that automatically locks onto a single station, preventing reception of other broadcasts

Why do we rely on social media algorithms to shape our views? Recent data show that 68% of young adults scroll feeds selected by algorithmic curation without verifying content. Lara noticed that her recommended posts reinforced her opinions, limiting exposure to different perspectives. She started checking multiple news sites manually, comparing viewpoints and noting discrepancies. Her friend Marco still trusts algorithm suggestions and rarely visits other sources. Meanwhile, Ana uses a browser extension that hides personalized recommendations, and Tom unfollows accounts he disagrees with without seeking context. These varied responses illustrate how digital tools can influence individuals’ information habits and social practices. Analise the user’s actions described and identify which practice best demonstrates critical engagement with digital algorithms?

1. A) Trusting algorithm suggestions as the primary news source
2. B) Comparing viewpoints across multiple news sites manually
3. C) Unfollowing disagreeable accounts without seeking context
4. D) Relying on algorithmic recommendations without source verification
5. E) Using a browser extension to hide personalized recommendations