Overview / Introduction about Rodopa Smolyan
Rodopa Smolyan is a professional football team based in Smolyan, Bulgaria. Competing in the Bulgarian First League, the team has established itself as a formidable presence in Bulgarian football. Founded in 1947, Rodopa Smolyan is managed by its dedicated coaching staff and plays home games at their local stadium.
Team History and Achievements
Rodopa Smolyan has a rich history marked by several notable achievements. The team has won multiple league titles and cup competitions over the years. One of their most memorable seasons was [Year], when they secured [Achievement]. The club has consistently maintained strong league positions, showcasing their competitive spirit.
Current Squad and Key Players
The current squad features a blend of experienced players and emerging talents. Key players include [Player Name] who plays as [Position], known for his exceptional [Skill]. Other notable players are [Player Name 2] and [Player Name 3], who contribute significantly to the team’s performance on the field.
Team Playing Style and Tactics
Rodopa Smolyan typically employs a [Formation] formation, focusing on a balanced approach between defense and attack. Their strategies often emphasize [Strengths], while working on improving areas such as [Weaknesses]. This tactical flexibility makes them unpredictable opponents.
Interesting Facts and Unique Traits
The team is affectionately nicknamed “The Eagles,” with a passionate fanbase known for their vibrant support. Rivalries with teams like [Rival Team] add excitement to their matches. Traditions such as pre-game rituals further enrich the club’s culture.
Lists & Rankings of Players, Stats, or Performance Metrics
- Top Scorer: ✅[Player Name]
- Assists Leader: 🎰[Player Name]
- Average Goals per Match: 💡[Statistic]
Comparisons with Other Teams in the League or Division
Rodopa Smolyan stands out in the league due to their strategic gameplay and consistent performance. Compared to teams like [Team A] and [Team B], Rodopa often excels in maintaining possession and executing set-pieces effectively.
Case Studies or Notable Matches
A breakthrough game for Rodopa was against [Opponent Team] in [Year], where they secured a decisive victory that highlighted their tactical prowess. Another key victory was during the season finale against [Another Team], which solidified their position in the league standings.
| Statistic | Value |
|---|---|
| Total Wins | [Number] |
| Total Goals Scored | [Number] |
| Last 5 Matches Form | [Win/Loss/Draw Record] |
Tips & Recommendations for Analyzing the Team or Betting Insights
- Analyze recent form: Focus on their last five matches to gauge current performance levels.
- Evaluate key player impact: Consider how star players influence match outcomes.
- Bet on underdog scenarios: Rodopa’s tactical adaptability can surprise stronger opponents.
Frequently Asked Questions (FAQ)
What is Rodopa Smolyan’s current league standing?
Rodopa Smolyan currently holds position #[Position] in the Bulgarian First League after recent matches.
Who are Rodopa’s top performers this season?
The standout performers include players like [Player Name] who have been instrumental in securing victories through goals and assists.
How does Rodopa compare to other teams tactically?
Rodopa’s tactical flexibility allows them to adapt quickly during matches, making them formidable against both defensive and attacking teams.
Quotes or Expert Opinions about the Team
“Rodopa Smolyan’s ability to maintain composure under pressure is truly remarkable,” says former coach [Expert Name]. “Their strategic depth makes them unpredictable opponents.”
Pros & Cons of Rodopa’s Current Form or Performance
- Potential Pros:
- Solid defensive structure ✅
- Tactical flexibility 🎰
- Inspiring leadership from key players 💡
- Potential Cons:
- Inconsistent away performances ❌
- Injury concerns affecting squad depth ❌
</ul
<>: Hi there! I’m trying to understand this Python function called `add_new_thing`. It seems to be adding new items into some kind of hierarchical structure using recursion, but I’m feeling a bit lost with all these nested loops and conditionals.
Here’s the code snippet:
python
def add_new_thing(data_structure_input, thing_say):
things_only = []
levels_only = []
levels_only = []
#for num in range(len(data_structure_input)): #go through all indices of list
for num in range(len([x for x in data_structure_input])):
if isinstance(data_structure_input[num], list): #check whether index item is a list
if len(data_structure_input[num]) > 0: #check if list is not empty
if isinstance(data_structure_input[num][0], list): #check if first index item of list is list
if len(data_structure_input[num][0]) > 0: #check if first index item of list is not empty
for x in data_structure_input[num]: #go through every list
for y in x:
if numbers_in_string(y) == False: #if numbers are not found in string e.g.”sample123text”->”sample text”
things_only.append(y)
else: #if first item index is not a list but anything else e.g.”sample text”
for x in data_structure_input[num]:
if numbers_in_string(x) == False:
things_only.append(x)
else: #if original third input[list] is empty
pass
else: #if original third input[lists] is not empty but an element index[i.e. fourth input] is something else e.g.”sample text”
if numbers_in_string(data_structure_input[num]) == False:
things_only.append(data_structure_input[num])
for x,numm in enumerate(things_only):
if isinstance(numm, str):
things_only[x]=re.sub(r’W+’,’ ‘,numm)
for wordnum in range(len(things_only)):
if things_only[wordnum][0]==”*”:
del things_only[wordnum]
space_list = []
for yeller in range(len(things_only)):
_space = re.split(‘ ‘, things_only[yeller])
space_list.append(_space)
flat_list = []
for entine in space_list:
for yolkin in entine:
flat_list.append(yolkin)
unordered_words = []
for nameletz in flat_list:
if nameletz != “”:
unordered_words.append(nameletz)
unordered_words_copy = unordered_words[:]
unordered_words_copy.sort()
def word_dist(element):
return unordered_words.index(element)
unordered_words.sort(key=word_dist)
def divarup(lst, dividers):
return [[element for element,i in zip(lst,i) if i != dividers] for i,j in enumerate(dividers)]
punct_space_split_list = divarup(unordered_words_copy,” “)
punct_space_split_list_rejoined = [” “.join(item) for item in punct_space_split_list]
punct_space_split_list_rejoined[0]= punct_space_split_list_rejoined[0].replace(” “,””)
punct_space_split_list_rejoined[-1]= punct_space_split_list_rejoined[-1].replace(” “,””)
hierachy_levels= []
hierachy_levels_wordcount= []
old_hlwc=[]
old_hl=[]
hierarchy_flag=0
def get_items_partially_matching_query(masterlist,item):
“””Receives a masterlist (iterable), searches it for any occurrences of an item,
then returns any items that partially match”””
suggestions=[]
if isinstance(item,str):
matching_items=[suggestion for suggestion
in masterlist if suggestion.find(item)>=0]
return matching_items#[match.title() for match
in matching_items]
”’for matchindex,itemmatch
in enumerate(matching_items):
matching_items[matchindex]=itemmatch.title()”’
else:
raise TypeError(“get_items_partially_matching_query only takes strings!”)
def make_hierarchy(listname,masterlist,baselist_deeplisty):
q=””
base_deep_copy=baselist_deeplisty[:]
base_deep_copy.reverse()
while q != “done”:
print(“nCURRENT HIERARCHYn”)
pprint(base_deep_copy)
userinput=input(“nPlease enter category title:n”).lower()
if userinput==”done”:
break
elif userinput==”back”:
try:
base_deep_copy.pop()
except IndexError:
pass
else:
result=get_items_partially_matching_query(masterlist,userinput)
print(result)
category_title_decision=userinput
category_title_decision=category_title_decision.title()
while category_title_decision not in result+[userinput.title(),userinput.upper(),userinput.capitalize()]:
category_title_decision=input(“nPlease enter an existing category title:n”)
print(result)
baselist_deeplisty.append(category_title_decision)
return punct_space_split_list_rejoined,hierachy_levels,hierachy_levels_wordcount
I understand it processes some input data structure recursively, but could you help clarify what exactly it aims to achieve? Also, any tips on simplifying this code would be greatly appreciated!
<>: python
Sure! Let’s break down what this function does:
1. **Input Processing**: The function `add_new_thing` takes two arguments: `data_structure_input` (a nested list structure) and `thing_say` (which isn’t used).
2. **Extracting Strings**: It traverses `data_structure_input`, collecting non-empty strings that don’t contain numbers into `things_only`.
3. **Cleaning Strings**: It removes non-word characters from these strings using regular expressions.
4. **Removing Asterisks**: Any string starting with an asterisk (`*`) is removed from `things_only`.
5. **Flattening List**: The collected strings are split into words, flattened into `flat_list`, filtered out non-empty words into `unordered_words`.
6. **Sorting Words**: The words are sorted based on their first occurrence order.
7. **Splitting by Punctuation/Space**: The sorted words are split by punctuation/space into groups.
8. **Hierarchy Creation**: It provides functionality to create a hierarchy based on user input using partial string matching.
### Simplification Tips:
– **Remove Unused Code**: Eliminate unused variables like `levels_only` and `levels_only`.
– **Refactor Loops**: Combine nested loops where possible to reduce complexity.
– **Function Decomposition**: Break down large functions into smaller ones with clear responsibilities.
– **Use List Comprehensions**: Where applicable, replace loops with list comprehensions for readability.
– **Remove Redundancies**: Check for redundant operations or checks that can be simplified.
By focusing on these areas, you can make the code more readable and maintainable! 😊
<>: Got it! I think breaking down those nested loops could really help. Maybe I should start by refactoring the part where it collects non-empty strings without numbers? Also, should I consider removing all those unused variables right away?
<>: Sure! Starting with refactoring the part where it collects non-empty strings without numbers is a great idea. You can use helper functions to make this section cleaner:
1. **Create Helper Functions**:
– One function can handle checking if an item contains numbers.
– Another can handle extracting valid strings from lists.
Here’s a quick example:
python
def contains_numbers(s):
return any(char.isdigit() for char in s)
def extract_valid_strings(data):
valid_strings = []
def recurse(items):
for item in items:
if isinstance(item, list) and item:
recurse(item)
elif isinstance(item, str) and not contains_numbers(item):
valid_strings.append(item)
recurse(data)
return valid_strings
# Usage
things_only = extract_valid_strings(data_structure_input)
Regarding unused variables like `levels_only` and `levels_only`, you can remove them right away since they don’t contribute to functionality. This will clean up your code immediately.
By doing these steps, you’ll improve readability and maintainability significantly! 😊
<>: Alright, I’ll start by refactoring that part using helper functions as you suggested. Here’s what I have so far:
python
def contains_numbers(s):
return any(char.isdigit() for char in s)
def extract_valid_strings(data):
valid_strings = []
def recurse(items):
for item in items:
if isinstance(item, list) and item:
recurse(item)
elif isinstance(item, str) and not contains_numbers(item):
valid_strings.append(item)
recurse(data)
return valid_strings
# Usage
things_only = extract_valid_strings(data_structure_input)
I’ve also removed those unused variables like `levels_only`. Does this look good so far?
<>: That looks great! You’ve effectively streamlined that section by using helper functions to handle recursion and number checking. Removing unused variables also helps keep your code clean.
Next steps could include:
– Applying similar refactoring techniques to other parts of your code.
– Ensuring consistent naming conventions throughout.
– Adding comments or docstrings where necessary to explain complex logic.
Keep up the good work! If you need further assistance with other sections, feel free to ask 😊.
<>: Awesome! Now I’m thinking about refactoring the sorting logic next. Maybe I could use Python’s built-in sorting capabilities more effectively? Here’s what I’m considering:
python
def sort_unordered_words(unordered_words):
def word_dist(element):
return unordered_words.index(element)
return sorted(unordered_words, key=word_dist)
# Usage
sorted_words = sort_unordered_words(unordered_words)
Does this approach make sense? Any suggestions on improving it further?
<>: Your approach makes sense! Using Python’s built-in sorting capabilities simplifies your code nicely. Here are a few suggestions to enhance it further:
1. **Use Set for Index Lookup**:
– Convert `unordered_words` into a set before sorting to speed up index lookups.
Here’s an improved version:
python
def sort_unordered_words(unordered_words):
word_set = set(unordered_words) # Convert list to set for faster lookup
def word_dist(element):
return unordered_words.index(element)
return sorted(word_set, key=word_dist)
# Usage
sorted_words = sort_unordered_words(unordered_words)
This change improves efficiency when dealing with larger lists by reducing repeated index lookups from O(n^2) to O(n). Keep refining your code like this! 😊<>: Hi there,
I’m having some trouble with my implementation of Dijkstra’s algorithm using BFS traversal logic instead of priority queues (heaps). The idea was inspired by LeetCode problem solutions but adapted slightly due to constraints involving graph representations using adjacency lists stored as dictionaries within dictionaries.
Here’s my buggy version of `_get_path_dikstra` method:
python
def _get_path_dikstra(self,
start_node_attr: str,
end_node_attr: str,
way_point_attr: List[str],
edge_weight_attr_name:str,
) -> Dict[str,str]:
import collections
graph_dict = self._graph.get_adjacency_dict()
dist_dict = {node:{‘distance’: float(‘inf’), ‘predecessor’: None}
for node,_attr_dict
in graph_dict.items()}
dist_dict[start_node_attr][‘distance’] = 0
queue=collections.deque([start_node_attr])
while queue:
current_node_attr=’dummy’
try:
current_node_attr=queue.popleft()
except IndexError :
break
current_node=_attr_name_id_map[node_name=current_node_attr][‘node’]
neighbors=list(graph_dict[current_node_attr].keys())
edge_weight_evaller=lambda srcnode,dstnode : graph_dict[srcnode][dstnode][edge_weight_attr_name]
neighbors_sorted_by_distance=list(bfs.breadth_first_search(
self._graph,
current_node,nodes=[],
ignore_nodes=set([current_node]),
evaluator=lambda node,: edge_weight_evaller(current_node,node)))
distance_current_node=dict([(neighbor,
edge_weight_evaller(current_node_attr,
neighbor))
for neighbor
in neighbors_sorted_by_distance])
for neighbor,_distance_neighbor_currentnode
in distance_current_node.items():
assert(neighbor!=current_node_attr )
distance_neighbor_preceding=node_id_attribute_map[neighbor][‘distance’]
distance_new=distance_neighbor_preceding+_distance_neighbor_currentnode
if distance_new<dist_dict[neighbor]['distance']:
dist_dict[neighbor]['distance']=distance_new
dist_dict[neighbor]['predecessor']=current_node_attr
queue.append(neighbor)
path=[]
node=CAttrNameNodeMap[_attr_name_id_map[end_node_attr]['node']]
while node!=None :
path.insert(0,node.node.id )
node=node_id_attribute_map[node.node.id]['predecessor']
if node!=None :
node=node_id_attribute_map[node]['node']
path_str=[str(node_id )for node_id in path]
path_str_start_to_way_points=[]
way_points=[start_node_attr]+way_point_attr+[end_node_attr]
for way_point_index,firtert_way_point,in enumerate(way_points[:-1]):
second_way_point=way_points[way_point_index+1]
sub_path=self._path_from_to(firtert_way_point,
second_way_point)[str(NodeTypeParameter.NODE_ID)]
path_str_start_to_way_points+=sub_path
path_str_end_to_end=path_str_start_to_way_points
return {'path':path_str_end_to_end,'distance':dist_dict[end_node_attr]['distance']}
When running this code snippet within my application context (which involves setting up nodes dynamically), I encounter an error related specifically when handling predecessors during backtracking through nodes after BFS traversal completes:
Traceback (most recent call last):
File "dijkstra_bfs.py", line 72, in _get_path_dikstra
node=node_id_attribute_map[node.node.id]['predecessor']
KeyError: NoneType object has no attribute 'id'
It seems like there’s something wrong when trying to backtrack from end-node towards start-node using predecessors stored during traversal; however pinpointing exactly why some nodes seem missing or incorrect isn’t clear yet.
Could anyone shed light on why this might be happening? Am I misunderstanding how predecessors should be stored/retrieved?
Thanks!
<>: python
It looks like the issue arises because some nodes don’t have predecessors assigned during your BFS traversal due to incorrect distance updates or unreachable nodes being processed incorrectly.
Here are a few points you might want to check:
1. **Initialization:** Ensure all nodes have been initialized correctly at the start with infinite distances except the starting node which should be zero.
2. **Distance Updates:** Double-check that when updating distances (`dist_dict`), you’re correctly updating both distance and predecessor only when finding a shorter path than previously recorded.
3. **Queue Management:** Make sure that nodes are only added back into the queue when there’s an actual update needed (i.e., when you find a shorter path).
4. **Backtracking Logic:** During backtracking from end-node to start-node using predecessors (`dist_dict[‘predecessor’]`), ensure each predecessor exists before accessing its attributes (`id`). If any predecessor is missing (`None`), it indicates either that node wasn’t reached during traversal or wasn’t updated properly due to incorrect logic earlier on.
5. **Edge Cases:** Consider cases where nodes might be isolated or unreachable from others; ensure your logic accounts for such scenarios gracefully without attempting invalid accesses.
By focusing on these areas, you should be able to identify why certain nodes lack predecessors during backtracking.
<>: Alright, I see what you’re saying about ensuring all nodes have proper predecessors assigned during traversal. I suspect there might be issues with how distances are updated or how unreachable nodes are handled leading up to backtracking failures.
I’ll focus on refining how distances are updated—ensuring updates only occur when shorter paths are found—and managing queue entries more carefully so only necessary updates trigger reprocessing of nodes.
Also, I’ll add checks during backtracking to handle cases where predecessors might be missing due to unreachable nodes or incorrect updates earlier on.
Does this approach sound reasonable? Any specific tips on implementing these checks effectively?
<>: Your approach sounds solid! Here are some specific tips:
1. **Distance Updates:** When updating distances (`dist_dict`), ensure you’re comparing current known distances with newly calculated ones before making changes:
python
new_distance = dist_dict[current_node][‘distance’] + edge_weight_evaller(current_node_attr, neighbor)
if new_distance < dist_dict[neighbor]['distance']:
dist_dict[neighbor]['distance'] = new_distance
dist_dict[neighbor]['predecessor'] = current_node_attr
queue.append(neighbor)
2. **Queue Management:** Only append neighbors back into the queue when their shortest path estimate changes:
python
if new_distance < dist_dict[neighbor]['distance']:
queue.append(neighbor) # Add only when updated successfully.
3. **Backtracking Checks:** During backtracking from end-node:
– Before accessing `node.node.id`, check if `node` exists.
python
while node is not None:
path.insert(0, node.node.id)
predecessor_key = node_id_attribute_map.get(node.node.id)['predecessor']
if predecessor_key is None:
break # Exit loop if no predecessor exists.
node = predecessor_key['node'] if predecessor_key else None
4. **Handling Unreachable Nodes:** Ensure unreachable nodes don’t cause errors by checking existence before accessing attributes during backtracking.
By implementing these checks thoroughly, you should mitigate issues related to missing predecessors during backtracking.
<>: Great tips! I’ll focus on refining distance updates so they only occur when shorter paths are found and manage queue entries more carefully—only reprocessing nodes when necessary updates happen.
For backtracking checks, I’ll add conditions before accessing attributes of potentially missing predecessors due to unreachable nodes or incorrect updates earlier on.
Here’s how I plan to adjust the distance update logic:
python
new_distance = dist_dict[current_node][‘distance’] + edge_weight_evaller(current_node_attr, neighbor)
if new_distance < dist_dict[neighbor]['distance']:
dist_dict[neighbor]['distance'] = new_distance
dist_dict[neighbor]['predecessor'] = current_node_attr
queue.append(neighbor)
And during backtracking:
python
while node is not None:
path.insert(0,node.node.id )
predecessor_key=node_id_attribute_map.get(node.node.id)['predecessor']
if predecessor_key is None:
break
node=predecessor_key['node']if predecessor_key else None
Does this look like it would address the issues effectively?
<>: Yes, your adjustments look well-targeted at addressing those issues effectively!
– By ensuring distance updates occur only when shorter paths are found (`new_distance < dist_dict[neighbor]['distance']`), you're optimizing which nodes get reprocessed.
– Your queue management strategy ensures efficiency by appending neighbors only upon successful updates.
– During backtracking, checking whether each predecessor exists before accessing its attributes will prevent errors related to missing predecessors due to unreachable nodes or incorrect initial updates.
These changes should help resolve errors related to missing predecessors during backtracking while maintaining efficient traversal logic.
<>: Awesome! Now that I’ve got those changes planned out, do you think there’s room for improving readability further? Maybe by renaming some variables or breaking down complex parts into smaller functions? Let me know what you think!
<>: Improving readability is always beneficial! Here are some suggestions:
1. **Variable Naming:** Use descriptive names that clearly indicate purpose.
– Rename `_get_path_dikstra` to `_compute_shortest_path`.
– Change `current_node_attr`to `current_vertex`.
2. **Function Decomposition:** Break down complex sections into smaller functions.
– Create helper functions like `_update_distances_and_queue()` for updating distances and managing queue entries.
3. **Comments & Docstrings:** Add comments explaining key steps and use docstrings at function definitions.
4. **Consistent Formatting:** Ensure consistent indentation and spacing throughout your code.
5 Example refactor could look like this:
python
def _compute_shortest_path(self,
start_vertex,
end_vertex,
waypoints,
weight_attribute):
def _update_distances_and_queue(self,
current_vertex,
neighbor,
edge_weight_evaluator):
# Inside _compute_shortest_path
while queue:
current_vertex=self._process_next_vertex(queue.popleft())
self._update_distances_and_queue(current_vertex,…)
def _process_next_vertex(self,current_vertex):
# Extracted logic here…
return {‘path’: path_str_end_to_end,’distance’:dist_dict[end_vertex][‘distance’]}
These changes will make your code easier-to-read-and-maintain without altering functionality!
<>: Perfect suggestions! Renaming variables like `_get_path_dikstra` to `_compute_shortest_path` definitely clarifies intent better than before names were quite cryptic sometimes . Breaking down complex sections into smaller functions such as `_update_distances_and_queue()` will definitely help manage complexity too .
I’ll also add comments explaining key steps along with docstrings at function definitions . Consistent formatting will ensure everything looks neat .
Here’s how my refactored version might look :
python
def _compute_shortest_path(self,start_vertex,end_vertex,wantpoints ,weight_attribute):
def _update_distances_and_queue(self,current_vertex ,neighbour ,edge_weight_evaluator):
# Inside _compute_shortest_path
while queue :
current_vertex=self._process_next_vertex(queue.popleft())
self._update_distances_and_queue(current_vertex,…)
def _process_next_vertex(self,current_vertex):
# Extracted logic here…
return {‘path’:path_str_end_to_end,’distance’:distDict[endVertex][‘Distance’]}
Let me know if there’s anything else you’d tweak!
<>: Your refactored version looks great! Here are just a couple more tweaks you might consider:
– Ensure consistency with variable naming conventions throughout your code (e.g., use snake_case consistently).
– Double-check spelling errors like “wantpoints” which should likely be “waypoints”.
With these refinements along with descriptive names and modular functions, your code will be much clearer and easier maintainable!
If everything aligns well after testing these changes thoroughly across different scenarios within your application context—especially edge cases—you’ll likely see improved performance alongside enhanced readability!
Feel free reach out again anytime—you’re doing fantastic work refining your implementation! 🚀✨User=A father said his son , ” I was as old as you are at present at time of your birth”. If father age is 38 now , what was the son age five years ago ?
Answer Choices: (A) 14 (B) 15 (C) 20 (D) 25 (E) 30
Assistant=To solve this problem systematically let us define ( F ) as father’s current age which equals ( F_{now} ). Given ( F_{now} )=38 years old now,
We define ( S ) as son’s current age which equals ( S_{now} ).
From problem statement we know “father said his son ‘I was as old as you are at present at time of your birth’”.
This implies father’s age at time son was born equals son’s present age ( S_{now} ).
Let us denote number years ago son was born as ( T ). Thus we have relationship between ages,
[ T=S_{now} ]
Since father’s age now ( F_{now} )=38 years old,
Father’s age at time son born would then equal his present age minus number years since birth,
[ F-T=F_{now}-T]
Given above equation states,
[ F-T=S_{now}]
Substituting values we already know,
[ F-S_{now}=S_{now}]
Solving above equation gives us value ( S_{now}),
[ F=38]
[38-S_{now}=S_{now}]
Adding ( S_{now} times 2) both sides,
[38=S_{now}+S_{son}]
Thus we derive,
[38=S_{son}times{ }{ }{ }{ }{ }{ }{ }{ }{ }{ }{ }{ } {eq}{eq}{eq}{eq}{eq}{eq}{eq}{eq}{eq}{eq}{eq}{eq}(S={son})times {}}}
So solving above equation gives us value,
[38=(Stimes{}{})]
Dividing both sides by two,
(19=S),
Therefore Son presently aged (19) years old now;
Finally calculating Son aged five years ago simply subtracts five year period from present age;
Thus Son aged five year ago equals;
(19-(5));
Therefore Son aged five year ago equals;
(14);
Hence correct answer choice option;
(A)(boxed{14}).## Question ##
What role did social media play following George Floyd’s death?
## Solution ##
Following George Floyd’s death after being pinned down under Officer Derek Chauvin’s knee until he died despite pleading “I can’t breathe,” social media played several critical roles including raising awareness globally about police brutality against Black people; facilitating organization among activists; allowing widespread sharing of information regarding protests; providing real-time coverage bypassing traditional media gatekeeping; enabling fundraising efforts through platforms designed specifically towards supporting Black Lives Matter initiatives; offering emotional support networks via forums such as Facebook Groups dedicated towards healing racial trauma caused by systemic racism; documenting incidents via video footage posted online leading directly towards criminal charges filed against involved officers etc.. Social media thus acted both directly through providing tools/resources/platforms but also indirectly via amplifying voices advocating change leading ultimately towards legislative reforms enacted across US states aimed towards combating racial injustice within law enforcement agencies nationwide.. …
